Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification

Zhang, Yong; Gong, Dunwei; Cheng, Jian

doi:10.1109/tcbb.2015.2476796

Cited by 300 publications

(93 citation statements)

References 47 publications

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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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Section: Related Workmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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“…These methods include Multiple Objective Genetic Algorithm (MOGA), Non-dominated Sorting Genetic Algorithm (NSGA), NSGA-II, Multi-objective Multi-state Genetic Algorithm (MOMS-GA), Niched-Pareto Genetic Algorithm (NPGA) and Multi-objective Particle Swarm Optimization (MOPSO) [9,21].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

Ma¹,

Yi²,

Qu³

et al. 2018

INT J COMPUT COMMUN

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Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better.

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“…Generally, a cost-based feature selection method is used to maximize the classification performance and minimize the classification cost associated with the features, which is a multi-objective optimization problem. Zhang et al [32] propose a cost-based feature selection method using multi-objective particle swarm optimization (PSO). The method generates a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications.…”

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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Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification

Cited by 300 publications

References 47 publications

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

EFS-MI: an ensemble feature selection method for classification

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