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A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology

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Abstract

Feature selection plays a key role in data mining and machine learning algorithms to reduce the processing time and increase the accuracy of classification of high dimensional datasets. One of the most common feature selection methods is the wrapper method that works on the feature set to reduce the number of features while improving the accuracy of the classification. In this paper, two different wrapper feature selection approaches are proposed based on Farmland Fertility Algorithm (FFA). Two binary versions of the FFA algorithm are proposed, denoted as BFFAS and BFFAG. The first version is based on the sigmoid function. In the second version, new operators called Binary Global Memory Update (BGMU) and Binary Local Memory Update (BLMU) and a dynamic mutation (DM) operator are used for binarization. Furthermore, the new approach (BFFAG) reduces the three parameters of the base algorithm (FFA) that are dynamically adjusted to maintain exploration and efficiency. Two proposed approaches have been compared with the basic meta-heuristic algorithms used in feature selection on 18 standard datasets. The results show better performance of the proposed approaches compared with the competing methods in terms of objective function value, the average number of selected features, and the classification accuracy. Also, the experiments on the emotion analysis dataset demonstrate the satisfactory results.

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Acknowledgments

The authors would like to thank the editor in chief, the associate editor, and reviewers for their constructive feedback during the review process. They genuinely appreciate the reviewers’ comments for making this paper more complete.

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Authors and Affiliations

  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Ali Hosseinalipour, Farhad Soleimanian Gharehchopogh & Mohammad Masdari

  2. Department of Psychology Science, Urmia Branch, Islamic Azad University, Urmia, Iran

    Ali Khademi

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  1. Ali Hosseinalipour
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  2. Farhad Soleimanian Gharehchopogh
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  3. Mohammad Masdari
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  4. Ali Khademi
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Correspondence to Farhad Soleimanian Gharehchopogh.

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Hosseinalipour, A., Gharehchopogh, F.S., Masdari, M. et al. A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Appl Intell 51, 4824–4859 (2021). https://doi.org/10.1007/s10489-020-02038-y

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