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This project addresses big data classification using hybrid Intelligence Classification System. Hybrid Intelligence classification system is a system that combines at least two intelligent technologies. Specifically, the focus of this project is to apply hybrid Neuro-Fuzzy system to the IBM Watson data and Innocentive Trustworthiness challenge data for prediction and classification. Neural network are lowlevel computational structure which has ability to learn and performs well on the raw data. On the other hand, fuzzy logic deals with reasoning on higher level using If-then rules and linguistic variables. So combining these two methods can provide us with a very powerful classification system. List of Figures: Fig1: Sample membership functions for feature one Fig2: Perceptron overview Fig3: Multi-Layer Neural Network Fig4: Project Design Overview for Neural Network Fig5: traingscgError vs. Epochs Fig6: traingscgGradient vs. Epochs Fig7: traingscgConfusion Matrix (Correct and Incorrect Classifications) Fig8: Improved traingscgError vs. Epochs Fig9: Improved traingscgGradient vs. Epochs Fig10: Improved traingscgConfusion Matrix (Correct and Incorrect Classifications) Fig11: Neuro Fuzzy system Structure Fig11:BackProp 3 memFunc-Error vs Epochs Fig12: BackProp 3 memFunc Gradient vs Epochs Fig13: BackProp 3 memFunc Confusion Matrix (Correct and Incorrect Classifications) Fig14: Zeroes in training dataset(x-axis: featuresy-axis: percentage of zeroes 0-100) Fig15: Zeroes in evaluation dataset(x-axis: featuresy-axis: percentage of zeroes 0-100) Fig16: Zeroes in training dataset(x-axis: featuresy-axis: percentage of zeroes 0-100) Fig17-Zeroes in evaluation dataset(x-axis: featuresy-axis: percentage of zeroes 0-100) Fig18: only zeroes-Error vs Epochs Fig19: only zeroes Gradient vs Epochs Fig20only zeroes Confusion Matrix (Correct and Incorrect Classifications) Fig21: combined 0s and 1s-Error vs Epochs Fig22combined 0s and 1s Confusion Matrix (Correct and Incorrect Classifications) Fig23: remove 0s column over 98%-Error vs Epochs Fig24: remove 0s column over 98%Gradient vs Epochs Fig25remove 0s column over 98% Confusion Matrix (Correct and Incorrect Classifications) Fig26:row selection -Error vs Epochs Fig27: row selection Gradient vs Epochs Fig28row selectionConfusion Matrix (Correct and Incorrect Classifications) Fig29: row & column combined -Error vs Epochs Fig30: row & column combined Gradient vs Epochs Fig31row & column combined Confusion Matrix (Correct and Incorrect Classifications) Fig32: IBM classifier 1 -Error vs Epochs Fig33: IBM classifier 1 Gradient vs Epochs Fig34IBM classifier 1 Confusion Matrix (Correct and Incorrect Classifications) Fig35: IBM classifier 2 -Error vs Epochs Fig36: IBM classifier 2 Gradient vs Epochs Fig37: IBM classifier 3 -Error vs Epochs Fig38: IBM classifier 3 Gradient vs Epochs Fig39IBM classifier 3 Confusion Matrix (Correct and Incorrect Classifications) Fig40: IBM classifier 4 -Error vs Epochs Fig41: IBM classifier 4 Gradient vs Epochs Fig42: Neuro Fuzzy 3 memFunc -Error vs Epochs Fig43: Neuro Fuzzy 3 memFunc Gradient vs Epochs Fig44Neuro Fuzzy 3 memFunc Confusion Matrix (Correct and Incorrect Classifications) Fig45: 4 classes dataset -Error vs Epochs Fig46: 4 classes dataset Gradient vs Epochs Fig474 classes dataset Confusion Matrix (Correct and Incorrect Classifications) Fig48: Trust classifier 1 -Error vs Epochs Fig49: Trust classifier 1 Gradient vs Epochs Fig50Trust classifier 1 Confusion Matrix (Correct and Incorrect Classifications) Fig51: Trust classifier 2 -Error vs Epochs Fig52: Trust classifier 2 Gradient vs Epochs Fig53Trust classifier 2 Confusion Matrix (Correct and Incorrect Classifications) Fig54: Trust classifier 3 -Error vs Epochs Fig55: Trust classifier 3 Gradient vs Epochs Fig56Trust classifier 3 Confusion Matrix (Correct and Incorrect Classifications) Fig57: Trust classifier 4 -Error vs Epochs Fig58: Trust classifier 4 Gradient vs Epochs Fig59Trust classifier 4 Confusion Matrix (Correct and Incorrect Classifications) Fig60: Retrain classifier Error vs Epochs Fig61: Retrain classifier Gradient vs Epochs Fig62Retrain classifier Confusion Matrix (Correct and Incorrect Classifications) List of Tables: Table1: Reduced weighted Fuzzy Classifier result on IBM dataset Table2: Comparison of trainscg method with different parameter Table3-Fuzzy System vs Neural Network Table4-feature Selection result Table5-Momentum learning with trapezoid membership functions Table6-classifier comparison using triangular membership functions Table7-classifier comparison using trapezoid membership functions Table8-classifier comparison using Gaussian membership functions Table8-classifier comparison using Gaussian membership functions on Trust datatset-79 hidden Table9-classifier comparison using Gaussian membership functions on Trust datatset-100 hidden Table10-classifier comparison using Gaussian membership functions on Trust datatset-56 hidden

Big Data Analysis Using Neuro-Fuzzy System