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

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

Abdiya Alaoui, Zakaria Elberrichi
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJIRR.2018100103
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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.
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Some features in the dataset may not be useful for diagnosis and thus can be eliminated before learning. This section explores the research works which are related to the proposed work.

A Novel Feature Selection Algorithm for Heart Disease Classification is proposed in (Subanya & Rajalaxmi, 2014). The Researchers uses a metaheuristic algorithm to determine the optimal feature subset with improved classification accuracy in heart disease diagnosis. A Binary Artificial Bee Colony (BABC) algorithm is used to find the best features in the disease identification. The fitness of BABC is evaluated using K–Nearest Neighbor (KNN) method.

Wang and Ma (2009) proposed a rough set-based feature selection for medical dataset. It says that rough set feature selection known as feature forest algorithm can improve classification accuracy.

Feature Selection Using Genetic Algorithm and Classification using Weka for Ovarian Cancer is proposed in (Khare & Burse, 2016). The aim of this paper to investigate the performance of different classification methods on clinical data. Before applying classification algorithm relevant feature are selected by applying genetic algorithm (Khare & Burse, 2016).

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