Classification of Heuristic Information by Using Machine Learning Algorithms

Abstract

The User Knowledge Modelling dataset in the UCI machine learning repository was used in this study. The students were classified into 4 class (very low, low, middle, and high) due to the 5 performance data in the dataset. 258 data of 403 data in the dataset were used for training and 145 of them were used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for classification. In classification Multilayer Perceptron (MLP), k Nearest Neighbors (kNN), J48, NativeBayes, BayesNet, KStar, RBFNetwork and RBFClassifier machine learning algorithms were used and success rates and error rates were calculated. In this study 8 different data mining algorithm were used and the best classification success rate was obtained by MLP. With Multilayer perceptron neural network model the classification success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best classification success rate was achieved as 97.2414% when there was 8 neurons in the hidden layer. MAE and RMSE values were obtained for this classification success rate as 0.0242 and 0.1094 respectively.

Keywords

• Machine learning,Weka,MLP,kNN,J48

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