Feature selection using genetics-based algorithm and its application to speaker identification

Demirekler, Mübeccel; Haydar, Ali

doi:10.1109/icassp.1999.758129

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…In addition, Section IV discusses the research on EC based filter approaches for feature selection. The applications of EC for feature selection are described in Section V. [37], [58], [38], [39], [44], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87] [88], [89], [90], [91], [92], [93], [94], [95], [96], [97] Filter [75], [98], [99], [100], [101], [102] [102],…”

Section: B Detailed Coverage Of This Papermentioning

confidence: 99%

“…Many different new enhancements to GAs have been proposed to improve the performance, which focus mainly on the search mechanisms, the representation, and the fitness function. Some early works [59], [62] introduced GAs to feature selection by investigating the influence of the population size, mutation, crossover, and reproduction operators, but with limited experiments.…”

Section: A Gas For Feature Selectionmentioning

confidence: 99%

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A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

1,472

635

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Section: B Detailed Coverage Of This Papermentioning

confidence: 99%

Section: A Gas For Feature Selectionmentioning

confidence: 99%

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

1,472

635

View full text Add to dashboard Cite

show abstract

“…The forerunner is Siedlecki and Sklansky who prove that genetic algorithm (GA) is a powerful tool for feature selection when the dimensionality of the given feature set is greater than 20 (Siedlecki and Sklansky 1993). After that, to further improve the performance of GA for feature selection, many different improvements have been proposed in search mechanisms (Demirekler and Haydar 1999;Jeong et al 2015;Wang et al 2020) and fitness function (Canuto and Nascimento 2012;Sousa et al 2013). A feature selection method (Derrac et al 2009) based on GA utilizes the cooperative coevolution (CC) framework (Potter and Jong 2000) but is not investigated in the large datasets.…”

Section: Related Workmentioning

confidence: 99%

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

et al. 2022

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Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of "curse of dimensionality" when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

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