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Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems

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Abstract

Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable alternatives to solve such problems in acceptable time. In the literature, a large number of algorithms have been proposed to solve the feature selection problem using meta-heuristic optimization algorithms. In this work, a new feature selection algorithm based on ARO meta-heuristic algorithm and DBSCAN clustering algorithm with automatic adjustment of input parameters (ARO-DBSCAN) is proposed. Using side algorithms to improve the performance of meta-heuristic algorithms can potentially cause getting stuck in local optima. The method proposed in the work has improved the performance of the ARO meta-heuristic for the feature selection problem without increasing the probability stuck in local optima. The use of DBSCAN clustering algorithm, which is based on density, increases the exploitation of ARO in the search space while maintaining its exploration. As a result, the performance of the ARO algorithm increases significantly in feature selection problems. The proposed algorithm is compared with 8 state-of-the-art feature selection algorithms on the UCI benchmark datasets and three real-world high-dimensional datasets. Result of experiments show the better performance of ARO-DBSCAN algorithm in the appropriate execution time. Also, in high-dimensional data, the proposed method is able to significantly reduce the number of dataset features. which makes the analysis of these datasets more efficient. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/ARO-DBSCAN.

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Data availability and access

The datasets analyzed during the current study are publicly available in the [UCI] repository, [https://archive.ics.uci.edu/datasets].

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Sciences, Damghan University, Damghan, Semnan, Iran

    Ali Hamdipour, Abdolali Basiri & Mostafa Zaare

  2. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, Australia

    Seyedali Mirjalili

  3. Faculty of Electrical Engineering and Computer Science, VŠB-TU Ostrava, 70080, Ostrava, Czech Republic

    Seyedali Mirjalili

Authors
  1. Ali Hamdipour
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Contributions

The authors confirm contribution to the paper as follows: Ali Hamdipour and Abdolali Basiri contributed to study conception and design; Ali Hamdipour and Mostafa Zaare contributed to data collection; Ali Hamdipour, Abdolali Basiri, and Mostafa Zaare contributed to analysis and interpretation of results; Ali Hamdipour contributed to draft manuscript preparation; Abdolali Basiri, Mostafa Zaare, Seyedali Mirjalili supervised the study; Abdolali Basiri and Seyedali Mirjalili contributed to review and edit. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Abdolali Basiri.

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We only used publicly available data, ensuring privacy. No private or personal information was used. Therefore, formal ethical approval and consent were not necessary. We followed all legal guidelines for research ethics.

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Appendices

Appendix A

Details of compressions

2.1 In term of classfication accuracy

The results obtained from BSMO feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that in two cases (BreastEW, Exactly2) BSMO algorithm had higher classification accuracy, in five cases (Breastcancer, PenglungEW, Wine, Tic-tac-toe, Zoo) they had same classification accuracy, and in the remaining cases (14 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than BSMO algorithm in classification accuracy.

The results obtained from WOASAT-2 feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that in any case WOASAT-2 algorithm had higher classification accuracy, in five cases (PenglungEW, Wine, Tic-tac-toe, GSAUFM, E-mails) they had same classification accuracy, and in the remaining cases (16 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than WOASAT-2 algorithm in classification accuracy.

The results obtained from SSD-LAHC feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that in two cases (BreastEW, Exactly2) SSD-LAHC algorithm had higher classification accuracy, in 9 cases (PenglungEW, BreastCancer, Exactly, M-of-n, Tic-tac-toe, Vote, Wine, Zoo, ARCENE) they had same classification accuracy, and in the remaining cases (10 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than SSD-LAHC algorithm in classification accuracy.

The results obtained from RTHS feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that RTHS algorithm had not higher classification accuracy in any case, in four cases (Tic-tac-toe, PenglungEW, Wine, Zoo, CongressEW) they had same classification accuracy, and in the remaining cases (16 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than RTHS algorithm in classification accuracy.

The results obtained from WOA-CM feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that in one cases (Exactly2) WOA-CM algorithm had higher classification accuracy, in three cases (Tic-tac-toe, PenglungEW, Wine) they had same classification accuracy, and in the remaining cases (17 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than WOA-CM algorithm in classification accuracy.

The results obtained from AAPSO feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that in one cases (Exactly2) AAPSO algorithm had higher classification accuracy, in four cases (Tic-tac-toe, Zoo, Wine, PenglungEW) they had same classification accuracy, and in the remaining cases (16 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than AAPSO algorithm in classification accuracy.

The results obtained from AIEOU feature selection algorithm in comparison with the ARO-DBSCAN in terms of classification accuracy show that in one cases (Exactly2) AIEOU algorithm had higher classification accuracy, in three cases (Tic-tac-toe, Wine, PenglungEW) they had same classification accuracy, and in the remaining cases (17 item) the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than AIEOU algorithm in terms of classification accuracy.

The results obtained from ASGW feature selection algorithm in comparison with the ARO-DBSCAN with respect to classification accuracy show that in the 21 cases the ARO-DBSCAN has obtained a higher classification accuracy. Therefore, in general, the ARO-DBSCAN has performed better than ASGW algorithm in classification accuracy.

2.2 In term of NSF

Table 7 presents the comparison results of the ARO-DBSCAN with satae-of-the-art algorithms from the aspect of NSF. The results obtained from BSMO feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in four cases (Ionosphere, WaveformEW, KrVsKpEW, SpectEW) BSMO algorithm had lower NSF, in two cases (BreastCancer, Tic-tac-toe) they had same NSF, and in the remaining cases (15 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than BSMO algorithm in NSF.

The results obtained from SSD-LAHC feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in five cases (Exactly, Zoo, Exactly2, WaveformEW, KrVsKpEW) SSD-LAHC algorithm had lower NSF, in four cases (BreastCancer, M-of-n, Tic-tac-toe, Exactly) they had same NSF, and in the remaining cases (12 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than SSD-LAHC algorithm in NSF.

The results obtained from WOA-CM feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in four cases (WaveformEW, KrVsKpEW, Zoo, CongressEW) WOA-CM algorithm had lower NSF, in one case (Tic-tac-toe) they had same NSF, and in the remaining cases (16 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than WOA-CM algorithm in NSF.

The results obtained from RTHS feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in four cases (BreastCancer, WaveformEW, KrVsKpEW) RTHS algorithm had lower NSF, in one case (Tic-tac-toe) they had same NSF, and in the remaining cases (17 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than RTHS algorithm in NSF.

The results obtained from AAPSO feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in three cases (Exactly2, WaveformEW, KrVsKpEW) AAPSO algorithm had lower NSF, in two cases (BreastCancer, Tic-tac-toe) they had same NSF, and in the remaining cases (16 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than AAPSO algorithm in NSF.

The results obtained from AIEOU feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in six cases (BreastCancer, Zoo, WaveformEW, KrVsKpEW, SpectEW, CongressEW) AIEOU algorithm had lower NSF, in one case (Tic-tac-toe) they had same NSF, and in the remaining cases (14 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than AIEOU algorithm in NSF.

The results obtained from ASGW feature selection algorithm in comparison with the ARO-DBSCAN with respect to NSF show that in three cases (Tic-tac-toe, WaveformEW, KrVsKpEW) ASGW algorithm had lower NSF and in the remaining cases (18 item) the ARO-DBSCAN has obtained a lower NSF. Therefore, in general, the ARO-DBSCAN has performed better than ASGW algorithm in NSF.

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Hamdipour, A., Basiri, A., Zaare, M. et al. Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems. J Supercomput 81, 150 (2025). https://doi.org/10.1007/s11227-024-06606-8

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