Based Data Mining Approach for Quality Control

divideification-Based entropy Mining Approach For woodland Control In booze Production GUIDED BY SUBMITTED BY Jayshri Patel Hardik Barfiwala INDEX Sr No Title Page No. 1 Introduction vino Production 2 Objectives 3 Introduction To information mess 4 Pre-Processing 5 Statistics character In algorithmic programic programic programic rules 6 Algorithms use On selective information assign 7 Comparison Of Applied Algorithm 8 Applying Testing Dataset 9 Achievements 1.INTRODUCTION TO WINE PRODUCTION * Wine industry is currently growing well in the market since the decease decade. However, the woodland factor in vino-colo violent has become the main(prenominal) burden in wine making and selling. * To meet the increasing demand, assessing the calibre of wine is necessary for the wine industry to prevent tampering of wine feature as well as maintaining it. * To remain competitive, wine industry is invest in mod technologies like info mine for analyzing taste and former(a) properties in wine. Data mining techniques provide more than summary, but semiprecious information such as patterns and relationships amid wine properties and human taste, alone of which stub be utilize to improve closing making and hone chances of success in some(prenominal) marketing and selling. * Two key elements in wine industry atomic number 18 wine certification and tint assessment, which be usu eithery conducted via physicochemical and sensory seeks. * Physicochemical tests be lab- found and argon use to characterize physicochemical properties in wine such as its niggardliness, inebriant or pH respects. * incriminatewhile, sensory tests such as taste druthers are performed by human experts.Taste is a special property that indicates flavour in wine, the success of wine industry impart be greatly determined by consumer satisfaction in taste requirements. * Physicochemical selective information are alike found useful in predicting human wi ne taste preference and affiliateifying wine based on aroma chromatograms. 2. heading * Modeling the complex human taste is an important focus in wine industries. * The main purpose of this study was to predict wine gauge based on physicochemical data. * This study was also conducted to identify reve all toldier or anomaly in sample wine set in rescript to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The insert content describes the source of data. * Source Of Data precedent to the experimental part of the research, the data is gathitherd. It is ga in that locationd from the UCI Data Repository. The UCI Repository of mold Learning Databases and Domain Theories is a free Internet repository of uninflected datasets from several areas. All datasets are in text files format provided with a short description. These datasets received recognition from mevery scientists and are claimed to be a val uable source of data. * Overview Of Dataset INFORMATION OF DATASETTitle Wine prime(a) Data limit symptomatics Multivariate function Of Instances WHITE-WINE 4898 RED-WINE 1599 stadium Business delegate Characteristic Real Number Of Attribute 11 + widening Attribute lacking Value N/A * Attribute Information * Input variables (based on physicochemical tests) * Fixed acidulousness come of Tartaric Acid present in wine. (In mg per liter) use for taste, feel and color of wine. * Volatile Acidity Amount of Acetic Acid present in wine. (In mg per liter) Its battlefront in wine is mainly due to yeast and bacterial metabolism. * citric Acid Amount of Citric Acid present in wine. In mg per liter) utilise to acidify wine that are too primary and as a flavor additive. * Residual Sugar The concentration of saccharide remaining after fermentation. (In grams per liter) * Chlorides Level of Chlorides added in wine. (In mg per liter) Used to coiffure mineral deficiencies in the br ewing water. * reconcile Sulfur Dioxide Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as preservative in wine process. * Density The density of wine is close to that of water, dry wine is less and sweet wine is grittyer. In kg per liter) * PH Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In protects) * Sulphates Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter) * Alcohol Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * step (score amidst 0 and 10) face cloth Wine 3 to 9 Red Wine 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried give away before mining the data to remove the antithetical lacks of the information in the data source .Following diametric process are carried out in the preprocessing reasons to make the dataset ready to perform contourification process. * Data in the real world is dirty because of the following reason. * Incomplete Lacking connect appraises, lacking certain portions of interest, or containing only aggregate data. * E. g. Occupation= * blatant Containing flaws or outliers. * E. g. Salary=-10 * Inconsistent Containing discrepancies in codes or names. * E. g. Age=42 Birthday=03/07/1997 * E. g. Was rating 1,2,3, Now rating A, B, C * E. g. Discrepancy between duplicate records * No quality data, no quality mining sequels tone decisions must be based on quality data. * Data store needs consistent integration of quality data. * Major Tasks in through in the Data Preprocessing are, * Data Cleaning * Fill in missing harbors, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of multiple databases, data cubes, or files. * The dataset provided from wedded data source is only in one single file. So in that location is no need for integrating the dataset. * Data transformation * Normalization and appeal * The dataset is in Normalized form because it is in single data file. * Data decrease Obtains reduced representation in volume but produces the same or same analytical results. * The data volume in the prone dataset is not very huge, the procedure of performing different algorithm is easily through on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numericalal data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only required in data pre-processing. * present, NumericToNominal, InterquartileRange and RemoveWithValues carrys are used for data pre-processing. * NumericToNominal Filter weka. filters. unsupervised. arrogate. NumericToNominal) * A fi lter for turning numeric attribute into nominal once. * In our dataset, section attribute fictitious character in both dataset (Red-wine note, White-wine Quality) form a type Numeric. So after applying this filter, order attribute Quality convert into type Nominal. * And Red-wine Quality dataset have class names 3, 4, 5 8 and White-wine Quality dataset have class names 3, 4, 5 9. * Because of sort does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and extreme economic take to bes based on interquartile retchs. The filter skips the class attribute. * Apply this filter for all attribute indices with all negligence alternatives. * After applying, filter adds two more handle which names are Outliers and ExtremeValue. And this fields has two types of label No and Yes. Here Yes label indicates, there are outliers and extreme value in dataset. * In our dataset, there are 83 extreme value and cxxv outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are AttributeIndex and NominalIndices. * AttributeIndex choose attribute to be use for option and NominalIndices choose range of label indices to be use for choice on nominal attribute. * In our dataset, AttributeIndex is last and NominalIndex is also last, so It will remove first 83 extreme values and indeed 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm RED-WINE RANKED WHITE-WINE Volatile_Acidity(2) 0. 1248 0. 0406 Volatile_Acidity(2) Tot al_sulfer_Dioxide(7) 0. 0695 0. 0600 Citric_Acidity(3) Sulphates(10) 0. 1464 0. 0740 Chlorides(5) Alcohal(11) 0. 2395 0. 0462 Free_Sulfer_Dioxide(6) 0. 1146 Density(8) 0. 2081 Alcohal(11) * The selection of attributes is performed automatically by WEKA using Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information apply with respect to the class. 5. STATISTICS USED IN ALGORITHMS * Statistics Measures there are Different algorithms that can be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics banners. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa coefficient, is a performance criterion or tycoon which compares the agreement from the representative with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance agreement. * Kappa statistic describe that our prediction for class attribute for given dataset is how much near to developed values. * Values Range For Kappa Range provide lt0 POOR 0-0. 20 SLIGHT 0. 21-0. 40 FAIR 0. 41-0. 60 MODERATE 0. 61-0. 80 SUBSTANTIAL 0. 81-1. 0 ALMOST PERFECT * As preceding(prenominal) range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accu order to material values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality Data constitute Algorithm White-wine Quality Red-wine Quality K-Star 0. 5365 0. 5294 J48 0. 3813 0. 3881 Multi story Perceptron 0. 2946 0. 3784 * have in mind unequivocal misapprehension (MAE) * cogitate controlling erroneousness (MAE)is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute misconduct is given by, toy with absolute misapprehension For Wine Quality Data fit out Algorithm White-wine Quality Red-wine Quality K-Star 0. 1297 0. 1381 J48 0. 1245 0 . 1401 Multilayer Perceptron 0. 1581 0. 1576 * settle Mean red-bloodedd illusion * If you have some data and try to make a toot (a formula) fit them, you can represent and see how close the crease is to the points. Another measure of how well the curve fits the data is etymon Mean square up break. * For all(prenominal) data point, CalGraph calculates the value ofy from the formula. It subtracts this from the datas y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, decide Mean Square wrongdoing is reconcile Mean shape wrongdoing For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 0. 2428 0. 2592 J48 0. 3194 0. 3354 Multilayer Perceptron 0. 2887 0. 3023 * commencement sexual relation form error * Theroot congenator square erroris relative to what it would have been if a simple predictor had been used. More specifica lly, this simple predictor is just the reasonable of the actual values. Thus, the relative shape error takes the total square up error and normalizes it by dividing by the total square error of the simple predictor. * By taking the square root of therelative square up errorone reduces the error to the same dimensions as the quantity being predicted. * Mathematically, theroot relative square errorEiof an individual programiis evaluated by the equation * whereP(ij)is the value predicted by the individual programifor sample casej(out ofnsample cases)Tjis the target value for sample casej andis given by the formula * For a perfect fit, the numerator is equal to 0 andEi= 0.So, theEiindex ranges from 0 to infinity, with 0 corresponding to the ideal. Root sexual congress form fallacy For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 78. 1984 % 79. 309 % J48 102. 9013 % 102. 602 % Multilayer Perceptron 93. 0018 % 92. 4895 % * Relative controlling Error * Therelative absolute erroris very similar to therelative squared errorin the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, therelative absolute errorEiof an individual programiis evaluated by the equation * whereP(ij)is the value predicted by the individual programifor sample casej(out ofnsample cases)Tjis the target value for sample casej andis given by the formula * For a perfect fit, the numerator is equal to 0 andEi= 0. So, theEiindex ranges from 0 to infinity, with 0 corresponding to the ideal.Relative supreme shape Error For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 67. 2423 % 64. 5286 % J48 64. 577 % 65. 4857 % Multilayer Perce ptron 81. 9951 % 73. 6593 % * Various evaluate * There are four-spot possible outcomes from a classifier. * If the outcome from a prediction ispand the actual value is alsop, then it is called a reliable positive(TP). * However if the actual value isnthen it is said to be a dark positive(FP). * Conversely, atrue negative(TN) has occurred when both the prediction outcome and the actual value aren. And counterfeit negative(FN) is when the prediction outcome isn while the actual value isp. * irresponsible Value P N TOTAL p True positive false positive P n false negative True negative N Total P N * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is all important(p) to measure the error rate of each method. * In the case of binary star classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver direct Characteristics is applied. * The sample ROC curve is presented in the Figure below.The closer the ROC curve is to the poll left corner of the ROC chart the mitigate the performance of the classifier. * Sample ROC curve (squares with the usage of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are applicable e. g. in decision tree models or rule sets. * Recall, clearcutness and F-Measure There are four possible results of classification. * Different crew of these four error and correct slurs are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of these classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classific ation are introduced. * The F measure is the harmonised mean of precision and recall. * The formal definitions of these measures are as follow PRECSION = TPTP+FP seclude = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * discombobulation matrix * A matrix used to summarize the results of a supervised classification. * Entries on the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K- close Neighbor courseifiers * Nearest neighbor classifiers are based on learning by analogy. * The planning samples are set forth by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an extraterrestrial being sample, a k- adjacent neighbor classifier searches the pattern space for the k training sample s that are closest to the unknow sample. * These k training samples are the k- nigh neighbors of the un cognise sample. Closeness is defined in terms of Euclidean space, where the Euclidean distance between two points, , * The unknown sample is charge the most habitual class among its k nearest neighbors. When k = 1, the unknown sample is claimed the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or unoccupied learners in that they store all of the training samples and do not progress a classifier until a hot (unlabeled) sample needs to be classified. * Lazy learners can incur dear(predicate) computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniques. As expected, lazy learning methods are faster at training than zealous methods, but slower at classification sin ce all computation is slow up to that judgment of conviction.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This may cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier - lazy tab. The dissolvent Generated After Applying K-Star On White-wine Quality Dataset Kstar Options -B 70 -M a measure interpreted To wee-wee Model 0. 02 Seconds class-conscious pass through-Validation (10-Fold) * Summary decent yrified Instances 3307 70. 6624 % wrong anatomyified Instances 1373 29. 3376 % Kappa Statistic 0. 5365 Mean Absolute Error 0. 1297 Root Mean Squared Error 0. 2428 Relative Absolute Error 67. 2423 % Root Relative Squared Error 78. 1984 % Total Number Of Instances 4680 * Detailed trueness By Class TP count FP Rate Precision Recall F-Measure ROC field of honor PRC firmament Class 0 0 0 0 0 0. 583 0. 004 3 0. 211 0. 002 0. 769 0. 211 0. 331 0. 884 0. 405 4 0. 672 0. 079 0. 777 0. 672 0. 721 0. 904 0. 826 5 0. 864 0. 378 0. 652 0. 864 0. 743 0. 84 0. 818 6 0. 536 0. 031 0. 797 0. 536 0. 641 0. 911 0. 772 7 0. 398 0. 002 0. 883 0. 398 0. 548 0. 913 0. 572 8 0 0 0 0 0 0. 84 0. 014 9 weighted Avg. 0. 707 0. 2 0. 725 0. 707 0. 695 0. 876 0. 787 * Confusion Matrix A B C D E F G Class 0 0 4 9 0 0 0 A=3 0 30 49 62 1 0 0 B=4 0 7 919 437 5 0 0 C=5 0 2 201 1822 81 2 0 D=6 0 0 9 389 468 7 0 E=7 0 0 0 73 30 68 0 F=8 0 0 0 3 2 0 0 G=9 * execution Of The Kstar With wonder To A Testing Configuration For The White-wine Quality DatasetTesting system Training Set Testing Set 10-Fold Cross Validation 66% Split a skilful categorize Instances 99. 6581 % 100 % 70. 6624 % 63. 9221 % Kappa statistic 0. 9949 1 0. 5365 0. 4252 Mean Absolute Error 0. 0575 0. 0788 0. 1297 0. 1379 Root Mean Squared Error 0. 1089 0. 145 0. 2428 0. 2568 Relative Absolute Error 29. 8022 % 67. 2423 % 71. 2445 % * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options -B 70 -M a Time Taken To Build Model 0 Seconds Stratified Cross-Validation (10-Fold) * Summary aright categorise Instances 1013 71. 379 % wrongly Classified Instances 413 28. 9621 % Kappa Statistic 0. 5294 Mean Absolute Error 0. 1381 Root Mean Squared Error 0. 2592 Relative Absolute Error 64. 5286 % Root Relative Squared Error 79. 309 % Total Number Of Instances 1426 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area PRC Area Class 0 0. 001 0 0 0 0. 574 0. 019 3 0 0. 003 0 0 0 0. 811 0. 114 4 0. 791 0. 176 0. 67 0. 791 0. 779 0. 894 0. 867 5 0. 769 0. 26 0. 668 0. 769 0. 715 0. 834 0. 788 6 0. 511 0. 032 0. 692 0. 511 0. 588 0. 936 0. 722 7 0. 125 0. 001 0. 5 0. 125 0. 2 0. 896 0. 142 8 Weighted Avg. 0. 71 0. 184 0. 685 0. 71 0. 693 0. 871 0. 78 * Confusion Matrix A B C D E F Class 0 1 4 1 0 0 A=3 1 0 30 17 0 0 B=4 0 2 477 120 4 0 C=5 0 1 103 444 29 0 D=6 0 0 8 76 90 2 E=7 0 0 0 7 7 2 F=8 Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing order Training Set Testing Set 10-Fold Cross Validation 66% Split decent Classified Instances 99. 7895 % 100 % 71. 0379 % 70. 7216 % Kappa statistic 0. 9967 1 0. 5294 0. 5154 Mean Absolute Error 0. 0338 0. 0436 0. 1381 0. 1439 Root Mean Squared Error 0. 0675 0. 0828 0. 2592 0. 2646 Relative Absolute Error 15. 8067 % 6 4. 5286 % 67. 4903 % * J48 finis Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree pertain the different attribute the complexifyes between the nodes arrange us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the concluding value (classification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm * In determine to classify a new item, it first needs to create a decision tree based on the attribute values of the available training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the outmatch is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling within its year have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then touch sensation for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the bulk of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. By checking all the single attributes and their values with those seen in the decision tree model, we can assign or predict the target value of this new instance. * The Result Generated After Applying J48 On White-wine Quality Dataset Time Taken To Build Model 1. 4 Seconds Stratified Cross-Validation (10-Fold) * Summary Correctly Classified Instances 2740 58. 547 % falsely Classified Instances 1940 41. 453 % Kappa Statistic 0. 3813 Mean Absolute Error 0. 1245 Root Mean Squared Error 0. 3194 Relative Absolute Error 64. 5770 % Root Relative Squared Error 102. 9013 % Total Number Of Instances 4680 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measur e ROC Area Class 0 0. 002 0 0 0 0. 30 3 0. 239 0. 020 0. 270 0. 239 0. 254 0. 699 4 0. 605 0. 169 0. 597 0. 605 0. 601 0. 763 5 0. 644 0. 312 0. 628 0. 644 0. 636 0. 689 6 0. 526 0. 099 0. 549 0. 526 0. 537 0. 766 7 0. 363 0. 022 0. 388 0. 363 0. 375 0. 75 8 0 0 0 0 0 0. 496 9 Weighted Avg. 0. 585 0. 21 0. 582 0. 585 0. 584 0. 727 * Confusion Matrix A B C D E F G Class 0 2 6 5 0 0 0 A=3 1 34 55 44 6 2 0 B=4 5 50 828 418 60 7 0 C=5 2 32 413 1357 261 43 0 D=6 7 76 286 459 44 0 E=7 1 1 10 49 48 62 0 F=8 0 0 0 1 2 2 0 G=9 * Performance Of The J48 With Respect To A Testing Configuration For The White-wine Quality Dataset Testing Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 90. 1923 % 70 % 58. 547 % 54. 8083 % Kappa statistic 0. 854 0. 6296 0. 3813 0. 33 Mean Absolute Error 0. 0426 0. 0961 0. 1245 0. 1347 Root Mean Squared Error 0. 1429 0. 2756 0. 3194 0. 3397 Relative Absolute Error 22. 0695 % 64. 577 % 69. 84 % * The Result Generated After Applying J48 On Red-wine Quality Dataset Time Taken To Build Model 0. 17 Seconds Stratified Cross-Validation * Summary Correctly Classified Instances 867 60. 7994 % incorrectly Classified Instances 559 39. 2006 % Kappa Statistic 0. 3881 Mean Absolute Error 0. 1401 Root Mean Squared Error 0. 3354 Relative Absolute Error 65. 4857 % Root Relative Squared Error 102. 602 % Total Number Of Instances 1426 * Detailed Accuracy By Class Tp Rate Fp Rate Precision Recall F-measure Roc Area Class 0 0. 004 0 0 0 0. 573 3 0. 063 0. 037 0. 056 0. 063 0. 059 0. 578 4 0. 721 0. 258 0. 672 0. 721 0. 696 0. 749 5 0. 57 0. 238 0. 62 0. 57 0. 594 0. 674 6 0. 563 0. 64 0. 553 0. 563 0. 558 0. 8 7 0. 063 0. 006 0. 1 0. 063 0. 077 0. 691 8 Weighted Avg. 0. 608 0. 214 0. 606 0. 608 0. 606 0. 718 * Confusion Matrix A B C D E F Class 0 2 1 2 1 0 A=3 2 3 25 15 3 0 B=4 1 26 435 122 17 2 C=5 2 21 167 329 53 5 D=6 0 2 16 57 99 2 E=7 0 0 3 6 6 1 F=8 Performance Of The J48 With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 91. 1641 % 80 % 60. 7994 % 62. 4742 % Kappa statistic 0. 8616 0. 6875 0. 3881 0. 3994 Mean Absolute Error 0. 0461 0. 0942 0. 1401 0. 1323 Root Mean Squared Error 0. 1518 0. 2618 0. 3354 0. 3262 Relative Absolute Error 21. 5362 % 39. 3598 % 65. 4857 % 62. 052 % * Multilayer Perceptron * The back propagation algorithm performs learning on a multilayer feed- foregoing aflutter lucre. It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward nervous network consists of an enter layer, one or more hidden layers, and an takings layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measurable for e ach training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of neuronlike units, known as a hidden layer. The outfits of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the networks prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-wine Quality Dataset Time taken to strain model 36. 22 seconds Stratified cross-validation * Summary Correctly Classified Instances 2598 55. 5128 % Incorrectly Classified Instances 2082 44. 4872 % Kappa statistic 0. 2946 Mean absolute error 0. 1581 Root mean squared error 0. 2887 Relative absolute error 81. 9951 % Root relative squared error 93. 0018 % Total Number of Instances 4680 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area PRC Area Class 0 0 0 0 0 0. 344 0. 002 3 0. 056 0. 004 0. 308 0. 056 0. 095 0. 732 0. 156 4 0. 594 0. 165 0. 597 0. 594 0. 595 0. 98 0. 584 5 0. 704 0. 482 0. 545 0. 704 0. 614 0. 647 0. 568 6 0. 326 0. 07 0. 517 0. 326 0. 4 0. 808 0. 474 7 0. 058 0. 002 0. 5 0. 058 0. 105 0. 8 0. 169 8 0 0 0 0 0 0. 356 0. 001 9 Weighted Avg. 0. 555 0. 279 0. 544 0. 555 0. 532 0. 728 0. 526 * Confusion Matrix A B C D E F G Class 0 0 5 7 1 0 0 A=3 0 8 82 50 2 0 0 B=4 0 11 812 532 12 1 0 C=5 0 6 425 1483 188 6 0 D=6 0 1 33 551 285 3 0 E=7 0 0 3 98 60 10 0 F=8 0 0 0 2 3 0 0 G=9 * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method Training Set Testing Set 10-Fold Cross Validation 66% Split Correctly Classified Instances 58. 1838 % 50 % 55. 5128 % 51. 3514 % Kappa statistic 0. 3701 0. 3671 0. 2946 0. 2454 Mean Absolute Error 0. 1529 0. 1746 0. 1581 0. 1628 Root Mean Squared Error 0. 2808 0. 3256 0. 2887 02972 Relative Absolute Error 79. 2713 % 81. 9951 % 84. 1402 % * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model 9. 14 seconds Stratified cross-validation (10-Fold) * Summary Correctly Classified Instances 880 61. 111 % Incorrectly Classified Instances 54 6 38. 2889 % Kappa statistic 0. 3784 Mean absolute error 0. 1576 Root mean squared error 0. 3023 Relative absolute error 73. 6593 % Root relative squared error 92. 4895 % Total Number of Instances 1426 * Detailed Accuracy By Class TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0 0 0 0 0 0. 47 3 0. 42 0. 005 0. 222 0. 042 0. 070 0. 735 4 0. 723 0. 249 0. 680 0. 723 0. 701 0. 801 5 0. 640 0. 322 0. 575 0. 640 0. 605 0. 692 6 0. 415 0. 049 0. 545 0. 415 0. 471 0. 831 7 0 0 0 0 0 0. 853 8 Weighted Avg. 0. 617 0. 242 0. 595 0. 617 0. 602 0. 758 * Confusion Matrix A B C D E F Class 0 5 1 0 0 A=3 0 2 34 11 1 0 B=4 0 2 436 160 5 0 C=5 0 5 156 369 47 0 D=6 0 0 10 93 73 0 E=7 0 0 0 8 8 0 F=8 * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method Training Set Testing Set 10-Fold Cross V alidation 66% Split Correctly Classified Instances 68. 7237 % 70 % 61. 7111 % 58. 7629 % Kappa statistic 0. 4895 0. 5588 0. 3784 0. 327 Mean Absolute Error 0. 426 0. 1232 0. 1576 0. 1647 Root Mean Squared Error 0. 2715 0. 2424 0. 3023 0. 3029 Relative Absolute Error 66. 6774 % 51. 4904 % 73. 6593 % 77. 2484 % * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality usingKstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier achieved 61. 7111% accuracy. For the white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs rectify in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF distinct ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar J48 Multilayer Perceptron Time (Sec) 0 1. 08 35. 14 Kappa Statistics 0. 5365 0. 3813 0. 29 Correctly Classified Instances (%) 70. 6624 58. 547 55. 128 True Positive Rate (Avg) 0. 707 0. 585 0. 555 False Positive Rate (Avg) 0. 2 0. 21 0. 279 * Chart Shows The Best Suited Algorithm For Our Dataset (Measures Vs Algorithms) * In in a higher place chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart newspaper column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above chart you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar J48 Multilayer Perceptron Time (Sec) 0 0. 24 9. 3 Kappa Statistics 0. 5294 0. 3881 0. 3784 Correctly Classified Instances (%) 71. 0379 60. 6994 61. 7111 True Positive Rate (Avg) 0. 71 0. 608 0. 617 False Positive Rate (Avg) 0. 184 0. 214 0. 242 * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kap pa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING TESTING DATASET dance step1 Load pre-processed dataset. footfall2 Go to classify tab. Click on choose button and select lazy folder from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. standard3 Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ? in the cell of the corresponding raw ) as shown below. Step 4 make it this data set as . rff file. Step 5 From test option panel select supplied test set, click on to the set button and open the test dataset file which was lastly created by you from the disk. Step 6 From Result list panel panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click Re-evalua te model on current test set Step 7 Again right click on Kstar algorithm and select visualize classifier error Step 8Click on publish button and then save your test model.Step 9 After you had saved your test model, a order file is created in which you will be having your predicted values for your testing dataset. Step 10 Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. deed * Classification models may be used as part of decision support system in different stages of wine production, hence heavy(a) the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citr ic acid, and vaporizable acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I suggest that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also ranked alcohol as the highest in both datasets, hence the alcohol level is the main attribute that determines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.