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

Kaynakça

1. Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Retrieved 2010-12-09.
2. B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
3. Superby J. F., Vandamme J. P., Meskens N. Determination of factors influencing the achievement of the first-year university students using data mining methods. In International conference on intelligent tutoring systems, Educational Data Mining Workshop, Taiwan, 2006:1 – 8.
4. Márquez-Vera, C., Cano, A., Romero, C., Ventura, S. Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data, Applied Intelligence, April 2013, Volume 38, Issue 3, pp 315-330.
5. Sen B., Ucar E., Delen D. Predicting and analyzing secondary education placement-test scores: A data mining approach, Expert Systems with Applications, Vol. 39, No. 10, pp. 9468-9476, 2012.
6. WEKA, http://www.cs.waikato.ac.nz/~ml/weka/ Last access: 10.04.2015.
7. Rohit Arora and Suman, Comparative Analysis of Classification Algorithms on Different Datasets using WEKA, International Journal of Computer Applications (0975 – 8887) Volume 54– No.13, September 2012.
8. J. Wang, P. Neskovic, and L. N. Cooper, “Improving nearest neighbor rule with a simple adaptive distance measure”, Pattern Recognition Letters, 28(2):207-213, 2007.

9. Y. Zhou, Y. Li, and S. Xia, “An improved KNN text classification algorithm based on clustering”, Journal of computers, 4(3):230-237, 2009.
10. John G. Cleary, Leonard E. Trigg: “K*: An Instance based Learner Using an Entropic Distance Measure”, 12th International Conference on Machine Learning, 108-114, 1995.