Combined Algorithms for Classification of Conduct Disorder of Students in Thai Vocational School

Abstract

This research presents approaches for combined classification by using attribute filtering with data classification. The performance comparisons of single and combined classification algorithms were used for classifying the conduct disorder of vocational students in Thailand. Single classification included the performance comparisons of four classifiers: 1) Naive Bayes 2) Bayesian Belief Network 3) C4.5 algorithm and 4) RIPPER algorithm. Combined classification included two steps: attributes filtration and data classification. First step was the attribute selection technique named genetic search. Then, results were assessed by using three evaluators: 1) Correlation-based Feature Selection (CFS) 2) Consistency-based Subset Evaluation and 3) Wrapper Subset Evaluation. Second step was the classification of data set by using selected attributed from the first step and four classification algorithms used for single classification. Next, the measurements of classification accuracy had been performed by using the k-fold Cross Validation technique for both single and combined classifications. The outperformed classification model from single and combined classifications was identified. The model was named Conduct Disorder Classification Model (CDCM). It was found that combined classification using genetic search and wrapper subset evaluator with Decision Tree (C4.5) algorithm, had the highest accuracy rate at 83.01%. As well, results from CDCM evaluation showed that factors associated with conduct disorders of students included (1) grade point average of high school, (2) gender, (3) age, (4) father income, (5) grade of typing I subject, (6) height, (7) blood group and (8) grade of business subject. Future works were also suggested.