Classification of Heuristic Information by Using Machine Learning Algorithms

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 252 - 254, 26.12.2016

Murat Koklu Kadir Sabancı Muhammed Fahri Unlersen

Öz

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.

Anahtar Kelimeler

Machine learning, Weka, MLP, kNN, J48

Kaynakça

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