Abstract
Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we formulate a joint feature selection problem to reduce the number of the selected features while enhancing the accuracy. An improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the formulated problem. IBPSO introduces a local search factor based on Lévy flight, a global search factor based on weighting inertia coefficient, a population diversity improvement factor based on mutation mechanism and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problems. Experiments based on 16 classical datasets are selected to test the effectiveness of the proposed IBPSO algorithm, and the results demonstrate that IBPSO has better performance than some other comparison algorithms.
Highlights
Machine learning has been widely applied in many practical applications such as data mining, text processing, pattern recognition and medical image analysis, and these fields often rely on the datasets with a large amount of data [1]
The performance of the proposed improved binary particle swarm optimization (IBPSO) is verified by 16 classical datasets, and several other algorithms are selected for comparisons
Refer to [56], we selected BreastEW dataset to tune the key parameters of the proposed IBPSO since this dataset has the median size compared to other datasets
Summary
Machine learning has been widely applied in many practical applications such as data mining, text processing, pattern recognition and medical image analysis, and these fields often rely on the datasets with a large amount of data [1]. Part of the features may be irrelevant or even misleading for the machine learning algorithms, which increase the computational overhead and reduce accuracy of classification especially for the high-dimensional datasets [2], [3]. Feature selections are useful methods because they can eliminate redundant noise from the datasets so that making the machine learning algorithms perform to execute faster and more efficient. Swarm intelligence algorithms are efficient heuristic search methods for the wrapper-based feature selection problems [11]. We propose an improved binary PSO (IBPSO) algorithm to solve the formulated feature selection problem. IBPSO introduces a local search operator, a global search operator, a population diversity improvement factor and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problem.
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