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Abdelrazek M, Abd Elaziz M, El-Baz AH. CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection. Sci Rep 2024; 14:701. [PMID: 38184680 PMCID: PMC10771514 DOI: 10.1038/s41598-023-50959-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024] Open
Abstract
In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.
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Affiliation(s)
- Mohammed Abdelrazek
- Department of Mathematics, Faculty of Science, Damietta University, New Damietta, 34517, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, UAE
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
| | - A H El-Baz
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, 34517, Egypt.
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Alweshah M, Aldabbas Y, Abu-Salih B, Oqeil S, Hasan HS, Alkhalaileh S, Kassaymeh S. Hybrid black widow optimization with iterated greedy algorithm for gene selection problems. Heliyon 2023; 9:e20133. [PMID: 37809602 PMCID: PMC10559925 DOI: 10.1016/j.heliyon.2023.e20133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the Iterated Greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification accuracy of 94.426, average fitness values of 0.061, and an average number of selected genes of 2933.767.
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Affiliation(s)
- Mohammed Alweshah
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Yasmeen Aldabbas
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Bilal Abu-Salih
- Department of Computer Science, King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
| | - Saleh Oqeil
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Hazem S. Hasan
- Department of Plant Production and Protection, Faculty of Agricultural Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Saleh Alkhalaileh
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Sofian Kassaymeh
- Software Engineering Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan
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Sadeghian Z, Akbari E, Nematzadeh H, Motameni H. A review of feature selection methods based on meta-heuristic algorithms. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2183267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Affiliation(s)
- Zohre Sadeghian
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Ebrahim Akbari
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Hossein Nematzadeh
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Homayun Motameni
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
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Akinola OA, Agushaka JO, Ezugwu AE. Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems. PLoS One 2022; 17:e0274850. [PMID: 36201524 PMCID: PMC9536540 DOI: 10.1371/journal.pone.0274850] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Abstract
Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model's accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features. This paper presents a binary version of the dwarf mongoose optimization called the BDMO algorithm to solve the high-dimensional feature selection problem. The effectiveness of this approach was validated using 18 high-dimensional datasets from the Arizona State University feature selection repository and compared the efficacy of the BDMO with other well-known feature selection techniques in the literature. The results show that the BDMO outperforms other methods producing the least average fitness value in 14 out of 18 datasets which means that it achieved 77.77% on the overall best fitness values. The result also shows BDMO demonstrating stability by returning the least standard deviation (SD) value in 13 of 18 datasets (72.22%). Furthermore, the study achieved higher validation accuracy in 15 of the 18 datasets (83.33%) over other methods. The proposed approach also yielded the highest validation accuracy attainable in the COIL20 and Leukemia datasets which vividly portray the superiority of the BDMO.
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Affiliation(s)
- Olatunji A. Akinola
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa
| | - Jeffrey O. Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa
- Department of Computer Science, Federal University of Lafia, Lafia, Nasarawa State, Nigeria
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa
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Akinola OA, Ezugwu AE, Oyelade ON, Agushaka JO. A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets. Sci Rep 2022; 12:14945. [PMID: 36056062 PMCID: PMC9440036 DOI: 10.1038/s41598-022-18993-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected.
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Affiliation(s)
- Olatunji A Akinola
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Absalom E Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
| | - Olaide N Oyelade
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Jeffrey O Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
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Akinola OO, Ezugwu AE, Agushaka JO, Zitar RA, Abualigah L. Multiclass feature selection with metaheuristic optimization algorithms: a review. Neural Comput Appl 2022; 34:19751-19790. [PMID: 36060097 PMCID: PMC9424068 DOI: 10.1007/s00521-022-07705-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
Abstract
Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.
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Affiliation(s)
- Olatunji O. Akinola
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Jeffrey O. Agushaka
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201 KwaZulu-Natal South Africa
| | - Raed Abu Zitar
- Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, 38044 Abu Dhabi, United Arab Emirates
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Inforsmation Technology, Middle East University, Amman, 11831 Jordan
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Nadimi-Shahraki MH, Zamani H, Mirjalili S. Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Comput Biol Med 2022; 148:105858. [PMID: 35868045 DOI: 10.1016/j.compbiomed.2022.105858] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 06/15/2022] [Accepted: 07/08/2022] [Indexed: 01/01/2023]
Abstract
The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.
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Affiliation(s)
- Mohammad H Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia.
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
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Pathak VK, Gangwar S, Singh R, Srivastava AK, Dikshit M. A comprehensive survey on the ant lion optimiser, variants and applications. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2093409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Vimal Kumar Pathak
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India
| | - Swati Gangwar
- Department of Mechanical Engineering, Netaji Subhash University of Technology, Dwarka, India
| | - Ramanpreet Singh
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Mithilesh Dikshit
- Department of Mechanical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM) Ahmedabad, Ahmedabad, India
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Rajendran S, Khalaf OI, Alotaibi Y, Alghamdi S. MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network. Sci Rep 2021; 11:24138. [PMID: 34921161 PMCID: PMC8683424 DOI: 10.1038/s41598-021-03019-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 11/26/2021] [Indexed: 11/17/2022] Open
Abstract
In recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.
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Affiliation(s)
- Surendran Rajendran
- Center for Artificial Intelligence and Research (CAIR), Chennai Institute of Technology, Chennai, India.
| | - Osamah Ibrahim Khalaf
- Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad, Iraq
| | - Youseef Alotaibi
- Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Saleh Alghamdi
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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An hybrid particle swarm optimization with crow search algorithm for feature selection. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100108] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Thawkar S, Sharma S, Khanna M, Singh LK. Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer. Comput Biol Med 2021; 139:104968. [PMID: 34735947 DOI: 10.1016/j.compbiomed.2021.104968] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 10/20/2022]
Abstract
The design and development of a computer-based system for breast cancer detection are largely reliant on feature selection techniques. These techniques are used to reduce the dimensionality of the feature space by removing irrelevant or redundant features from the original set. This article presents a hybrid feature selection method that is based on the Butterfly optimization algorithm (BOA) and the Ant Lion optimizer (ALO) to form a hybrid BOAALO method. The optimal subset of features chosen by BOAALO is utilized to predict the benign or malignant status of breast tissue using three classifiers: artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine. The goodness of the proposed method is tested using 651 mammogram images. The results show that BOAALO outperforms the original BOA and ALO in terms of accuracy, sensitivity, specificity, kappa value, type-I, and type-II error as well as the receiver operating characteristics curve. Additionally, the suggested method's robustness is assessed and compared to five well-known methods using a benchmark dataset. The experimental findings demonstrate that BOAALO achieves a high degree of accuracy with a minimum number of features. These results support the suggested method's applicability for breast cancer diagnosis.
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Affiliation(s)
- Shankar Thawkar
- Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India.
| | - Satish Sharma
- Department of Electronics and Computer Science, R. T. M. Nagpur University, Nagpur, Maharashtra, India
| | - Munish Khanna
- Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
| | - Law Kumar Singh
- Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
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Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06224-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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MRMR-SSA: a hybrid approach for optimal feature selection. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00608-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Khrissi L, El Akkad N, Satori H, Satori K. Clustering method and sine cosine algorithm for image segmentation. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00544-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Abstract
Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.
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Abstract
Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost.
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Emary E, Zawbaa HM, Sharawi M. Impact of Lèvy flight on modern meta-heuristic optimizers. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.11.033] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zawbaa HM, Schiano S, Perez-Gandarillas L, Grosan C, Michrafy A, Wu CY. Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms. ADV POWDER TECHNOL 2018. [DOI: 10.1016/j.apt.2018.11.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft comput 2018. [DOI: 10.1007/s00500-018-3282-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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