Improving binary crow search algorithm for feature selection
The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low...
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| Vydáno v: | Journal of intelligent systems Ročník 32; číslo 1; s. 1598 - 610 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin
De Gruyter
01.01.2023
Walter de Gruyter GmbH |
| Témata: | |
| ISSN: | 2191-026X, 0334-1860, 2191-026X |
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| Abstract | The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%),
-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time. |
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| AbstractList | The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time. The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G -mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time. The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), -mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time. |
| Author | Hamed Alnaish, Zakaria A. Algamal, Zakariya Yahya |
| Author_xml | – sequence: 1 givenname: Zakaria A. surname: Hamed Alnaish fullname: Hamed Alnaish, Zakaria A. email: zakriahamoalnaish@uomosul.edu.iq organization: College of Sciences, University of Mosul, 41001 Mosul, Iraq – sequence: 2 givenname: Zakariya Yahya surname: Algamal fullname: Algamal, Zakariya Yahya email: zakariya.algamal@uomosul.edu.iq organization: College of Engineering, University of Warith Al-Anbiyaa, 56001 Karbala, Iraq |
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| Cites_doi | 10.1016/j.asoc.2018.06.040 10.1007/s00521-017-2988-6 10.1007/s00521-022-07203-7 10.1016/j.swevo.2021.101022 10.1109/CEC.2018.8477975 10.1109/ACCESS.2021.3135805 10.1007/s00521-018-3688-6 10.1016/j.chemolab.2014.06.011 10.1016/j.procs.2020.03.420 10.1080/1062936X.2015.1040453 10.1109/ACCESS.2019.2897325 10.1016/j.compstruc.2016.03.001 10.1515/jisys-2019-0062 10.1109/JEEIT.2019.8717491 10.1155/2022/5974634 10.1007/s00500-019-03988-3 10.1016/j.chemolab.2015.08.015 10.1016/j.knosys.2021.107034 10.1016/j.neucom.2015.06.083 10.1016/j.eswa.2021.116431 10.1007/978-981-10-8863-6_9 10.1016/j.chemolab.2021.104288 10.1016/j.mlwa.2021.100108 10.1016/j.eswa.2020.114288 10.1007/978-981-15-5281-6_34 |
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| SubjectTerms | Accuracy Antiinfectives and antibacterials binary crow search algorithm Classification Computational efficiency Computing time Datasets Feature selection Glycoproteins Performance evaluation quasi-oppositional method Search algorithms |
| Title | Improving binary crow search algorithm for feature selection |
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| Volume | 32 |
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