Abstract
This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.
Highlights
Feature selection is one of the cornerstones in machine learning [1] to select the proper combination of features to best describe the target problem. e useful features are retained while the redundant features and irrelevant features are removed by feature selection
E filter method is to evaluate the correlation between variables to reduce feature size of dataset, and the evaluation process does not involve specific learning algorithms [4]. e embedded method embeds feature selection with classifiers, where the commonly embedded methods include support vector machine (SVM), ID3, C4.5, and Lasso, a least-squares regression method based on L1 regular terms [5]. e wrapper method uses a feature search component to produce feature subset and utilizes the specific classifier to evaluate the performance of different feature subsets until achieving termination conditions
Experimental results showed that the feature selection performance was improved without significant increase in computational cost
Summary
Feature selection is one of the cornerstones in machine learning [1] to select the proper combination of features to best describe the target problem. e useful features are retained while the redundant features and irrelevant features are removed by feature selection. E filter method is to evaluate the correlation between variables to reduce feature size of dataset, and the evaluation process does not involve specific learning algorithms [4]. Artificial bee colony (ABC) [12], as a recently proposed swarm intelligence metaheuristic [13], has been employed to address feature selection problems due to its promising efficiency and simple implementation. Keles and Kılıç [14] applied ABC to feature selection on SCADI dataset with 70 samples and 206 attributes. Seven features were selected from 206 features to classify the dataset with various classification methods. Kiliç and Keles [15] proposed ABC to select features on z-Alizadeh Sani dataset with 303 samples and 56 attributes, and the classification accuracy is enhanced on the original data. Many models are inapplicable to high-dimensional data [24]
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