Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

Zhang, Yong; Cheng, Shi; Shi, Yuhui; Gong, Dunwei; Zhao, Xinchao

doi:10.1016/j.eswa.2019.06.044

Cited by 163 publications

(55 citation statements)

References 57 publications

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“…Although, return cost binary firefly algorithm and a Pareto dominance technique was proposed in [47], and a set of public datasets was used for experiment. Recently in 2019, a multi-objective based artificial bee colony with two-archive (leader and external) was proposed with a purpose to minimize feature cost and maximize the classification accuracy [48]. The proposed method was compared against eight datasets with three multi-objective approaches and two traditional methods.…”

Section: Related Workmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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Feature selection or dimensionally reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multiobjective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multiobjective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

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Section: Related Workmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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“…The disadvantage in ABC is it suffers from premature convergence, thus, CS assist ABC to replace the not-so-good solutions into good solutions. Other than that, [21] has used two archive guided multi-objective artificial bee colony algorithm for cost-sensitive feature selection. Two archives which are leader and external archives are utilized to enhance the search capability of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm With Evolutionary Operators for Cancer Microarray Data

2020

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Microarray data play a huge role in recognizing a proper cancer diagnosis and classification. In most microarray data set consist of thousands of genes, but the majority number of genes are irrelevant to the diseases. An efficient algorithm for gene selection becomes important to deal with large microarray data. The main challenge is to analyze and select the relevant genes with maximum classification accuracy. Various algorithms were proposed for gene classification in previous studies, however, limited success was succeeded due to the selection of many genes in the high-dimensional microarray data. This study proposed and developed a hybrid multi-objective cuckoo search with evolutionary operators for gene selection. Evolutionary operators that are used in this article were double mutation and single crossover operators. The motivation behind this research is to improve the dimensions' values and explorative search abilities. Multi-objective cuckoo search with evolutionary operators employed the selection of informative genes among the high-dimensional cancer microarray data. Experiments were conducted on seven publicly available and high-dimensional cancer microarray data sets. These microarray data sets consist of approximately 2000 to 15000 genes. The results from the experiments concluded that the developed algorithm, multi-objective cuckoo search with evolutionary operators outperforms cuckoo search and multi-objective cuckoo search algorithms with a smaller number of selected significant genes.

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“…Various methods have been developed for the task of feature selection in the unsupervised setting. Most of existing works distinguish these algorithms into three groups, i.e., filter [2], [4], [10], wrapper and embedded approaches [11]- [13], in terms of different selection strategy. Moreover, with the absent of supervised information, one of the key problem for unsupervised feature selection is to design the appropriate criterion to guide the search of relevant and informative features.…”

Section: Related Work a Unsupervised Feature Selectionmentioning

confidence: 99%

Unsupervised Dual Learning for Feature and Instance Selection

Ren

Zhou

et al. 2020

IEEE Access

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Feature selection and instance selection are dual operations on a data matrix. Feature selection aims at selecting a subset of relevant and informative features from original feature space, while instance selection at identifying a subset of informative and representative instances. Most of previous studies address these two problems separately, such that irrelevant features (resp. outliers) may mislead the process of instance (resp. feature) selection. In this paper, we address the problem by doing feature and instance selection simultaneously. We propose a novel unified framework, which chooses instances and features simultaneously, such that 1)all the data can be reconstructed from the selected instances and features and 2) the global structure which is characterized by the sparse reconstruction coefficient is preserved. Experimental results on several benchmark data sets demonstrate the effectiveness of our proposed method.

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Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

Cited by 163 publications

References 57 publications

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm With Evolutionary Operators for Cancer Microarray Data

Unsupervised Dual Learning for Feature and Instance Selection

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