Accuracy-based learning classification system

Bikash Kanti Sarkar, Shib Sankar Sana and Kripasindhu Chaudhuri

International Journal of Information and Decision Sciences, 2010, vol. 2, issue 1, 68-86

Abstract: In order to implement a multi-category classification system, an efficient rule set is imperative for its investigation. In this paper, such a system is being introduced. In the first phase of its kind, the C4.5 rule induction algorithm is adopted to obtain useful rule set from classification problem, following a new data set partitioning approach. Next, the presented genetic algorithm (GA) is implemented to refine the learned rules in more efficient way. The resultant system has been compared with UCS (GA-based classification system) and C4.5 (non GA-based rule induction algorithm) on a number of benchmark data sets collected from UCI (University of California at Irvine) machine learning repository. Results demonstrate that the proposed genetic approach provides marked improvement in a number of cases.

Keywords: C4.5 algorithm; decision trees; genetic algorithms; accuracy-based learning classification systems; rule induction; data set partitioning; benchmarking; University of California at Irvine; UCI; machine learning; UCS classifier system; universities; higher education; USA; United States; information science. (search for similar items in EconPapers)
Date: 2010
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