Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data
Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achi...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/564 |
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| author | Mohammad H. Nadimi-Shahraki Zahra Asghari Varzaneh Hoda Zamani Seyedali Mirjalili |
| author_facet | Mohammad H. Nadimi-Shahraki Zahra Asghari Varzaneh Hoda Zamani Seyedali Mirjalili |
| author_sort | Mohammad H. Nadimi-Shahraki |
| collection | DOAJ |
| description | Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed starling murmuration optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study. |
| first_indexed | 2024-03-11T10:07:11Z |
| format | Article |
| id | doaj.art-bf249f810ad149b3978449fb3b4cb10b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:07:11Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bf249f810ad149b3978449fb3b4cb10b2023-11-16T14:58:45ZengMDPI AGApplied Sciences2076-34172022-12-0113156410.3390/app13010564Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical DataMohammad H. Nadimi-Shahraki0Zahra Asghari Varzaneh1Hoda Zamani2Seyedali Mirjalili3Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, IranDepartment of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman 7616914111, IranFaculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, IranCentre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006, AustraliaFeature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed starling murmuration optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study.https://www.mdpi.com/2076-3417/13/1/564disease diagnosismedical datafeature selectionbinary metaheuristic algorithmsstarling murmuration optimizer (SMO)transfer function |
| spellingShingle | Mohammad H. Nadimi-Shahraki Zahra Asghari Varzaneh Hoda Zamani Seyedali Mirjalili Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data Applied Sciences disease diagnosis medical data feature selection binary metaheuristic algorithms starling murmuration optimizer (SMO) transfer function |
| title | Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data |
| title_full | Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data |
| title_fullStr | Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data |
| title_full_unstemmed | Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data |
| title_short | Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data |
| title_sort | binary starling murmuration optimizer algorithm to select effective features from medical data |
| topic | disease diagnosis medical data feature selection binary metaheuristic algorithms starling murmuration optimizer (SMO) transfer function |
| url | https://www.mdpi.com/2076-3417/13/1/564 |
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