Feature Subset Selection Using Ant Colony Optimization for a Decision Trees Classification of Medical Data

Abdiya Alaoui and Zakaria Elberrichi
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Abdiya Alaoui: EEDIS Laboratory, Department of Computer Science, Djillali Liabes University, Sidi Bel Abbès, Algeria
Zakaria Elberrichi: EEDIS Laboratory, Department of Computer Science, Djillali Liabes University, Sidi Bel Abbès, Algeria

International Journal of Information Retrieval Research (IJIRR), 2018, vol. 8, issue 4, 39-50

Abstract: This article describes how a feature selection is a one of the most important assignments of the preprocessing step for medical data. It can extract a subset of features from a larger set and eliminate redundant, irrelevant or noisy features. The authors can reduce the cost of diagnosis by avoiding many tests by the selection of features which are important for the prediction of disease. Applied to the task of supervised classification, the authors build a robust learning models for disease prediction. The search for a subset of features is an NP-hard problem which can be solved by metaheuristics. In this article, the hybridization between the Ant Colony Optimization and Adaboost with Decision Trees (C4.5) to improve the classification is proposed. The experiments show the usefulness of the approach.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jirr00:v:8:y:2018:i:4:p:39-50

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