Feature selection algorithm based on bare bones particle swarm optimization

Zhang, Yong; Gong, Dunwei; Hu, Ying; Zhang, Wanqiu

doi:10.1016/j.neucom.2012.09.049

Cited by 222 publications

(96 citation statements)

References 27 publications

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“…In this study, the Friedman test and the multiple comparison approach are employed to testify whether the difference of the 9 EC based FS methods in terms of classification accuracy is significant 20,28 . The Friedman test is a non-parametric method which is used to compare the classification performance of different classifiers over multiple datasets by ranking each algorithm on each dataset 43 .…”

Section: Statistical Analysis Of Bbpso-acjmentioning

confidence: 99%

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Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Qiu¹

2018

IJCIS

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Feature selection (FS) is a crucial data pre-processing process in classification problems. It aims to reduce the dimensionality of the problem by eliminating irrelevant or redundant features while achieve similar or even higher classification accuracy than using all the features. As a variant of particle swarm optimization (PSO), Bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimizer. However, it also suffers from premature convergence like other PSO algorithms, especially in high-dimensional optimization problems. In order to improve its performance in FS problems, this paper proposes a novel BBPSO based FS method called BBPSO-ACJ. An adaptive chaotic jump strategy is designed to help the stagnated particles make a large change in their searching trajectory. It can enrich the search behavior of BBPSO and prevent the particles from being trapped into local attractors. A new global best updating mechanism is employed to reduce the size of obtained feature subset. The proposed BBPSO-ACJ is compared with eight evolutionary computation (EC) based wrapper methods and two filter methods on nine benchmark datasets with different number of dimensions and instances. The experimenta l results indicate that the proposed method can select the most discriminative features from the entire feature set and achieve significantly better classification performance than other comparative methods.

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Section: Statistical Analysis Of Bbpso-acjmentioning

confidence: 99%

“…Some researchers have adopted BBPSO for FS problem. Zhang et al 28 proposed a BBPSO with a new local leader updating strategy and uniform combination for FS problem. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Qiu¹

2018

IJCIS

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“…To solve this problem, Zhang et al [29] proposed a feature selection algorithm based on the bare bones particle swarm optimization, which considers the complexity of an algorithm due to additional parameters. Because the acquisition cost for each feature can be unequal, multiobjective particle swarm optimization approach for cost-based feature selection and return-costbased binary firefly algorithm for feature selection are also studied [30,31] which have another objective function of minimizing the cost sum of features.…”

Section: Related Workmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

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“…In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. A binary bare bones particle swarm optimization (BPSO) method is proposed to select an optimal subset of features [31]. In this method, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm.…”

Section: Related Workmentioning

confidence: 99%

EFS-MI: an ensemble feature selection method for classification

Hoque

Singh

Bhattacharyya

2017

Complex Intell. Syst.

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Feature selection methods have been used in various applications of machine learning, bioinformatics, pattern recognition and network traffic analysis. In high dimensional datasets, due to redundant features and curse of dimensionality, a learning method takes significant amount of time and performance of the model decreases. To overcome these problems, we use feature selection technique to select a subset of relevant and non-redundant features. But, most feature selection methods are unstable in nature, i.e., for different training datasets, a feature selection method selects different subsets of features that yields different classification accuracy. In this paper, we provide an ensemble feature selection method using feature-class and feature-feature mutual information to select an optimal subset of features by combining multiple subsets of features. The method is validated using four classifiers viz., decision trees, random forests, KNN and SVM on fourteen UCI, five gene expression and two network datasets.

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Feature selection algorithm based on bare bones particle swarm optimization

Cited by 222 publications

References 27 publications

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

EFS-MI: an ensemble feature selection method for classification

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