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Tahmouresi A, Rashedi E, Yaghoobi MM, Rezaei M. Gene selection using pyramid gravitational search algorithm. PLoS One 2022; 17:e0265351. [PMID: 35290401 PMCID: PMC8923457 DOI: 10.1371/journal.pone.0265351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022] Open
Abstract
Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient’s survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.
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Affiliation(s)
| | - Esmat Rashedi
- Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran
- * E-mail:
| | - Mohammad Mehdi Yaghoobi
- Department of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
| | - Masoud Rezaei
- Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran
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Dhiman G, Oliva D, Kaur A, Singh KK, Vimal S, Sharma A, Cengiz K. BEPO: A novel binary emperor penguin optimizer for automatic feature selection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106560] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Agrawal P, Ganesh T, Mohamed AW. A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05375-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106341] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Han X, Li D, Liu P, Wang L. Feature selection by recursive binary gravitational search algorithm optimization for cancer classification. Soft comput 2020. [DOI: 10.1007/s00500-019-04203-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Harandizadeh H, Armaghani DJ, Mohamad ET. Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04803-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, Fujita H. An evolutionary gravitational search-based feature selection. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.038 https://doi.org/10.1016/j.ins.2019.05.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, Fujita H. An evolutionary gravitational search-based feature selection. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.038] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Xu J, Wang Y, Mu H, Huang F. Feature genes selection based on fuzzy neighborhood conditional entropy. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-18100] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jiucheng Xu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China
- Engineering Technology Research Center for Computing Intelligence and Data Mining, Xinxiang, Henan, China
| | - Yun Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China
| | - Huiyu Mu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China
| | - Fangzhou Huang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China
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A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm. Symmetry (Basel) 2018. [DOI: 10.3390/sym10110609] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.
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An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.05.009] [Citation(s) in RCA: 390] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA. J INTELL ROBOT SYST 2017. [DOI: 10.1007/s10846-017-0759-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Gravitational search algorithm and K-means for simultaneous feature selection and data clustering: a multi-objective approach. Soft comput 2017. [DOI: 10.1007/s00500-017-2923-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Barani F, Mirhosseini M, Nezamabadi-pour H, Farsangi MM. Unit commitment by an improved binary quantum GSA. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.06.051] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zandevakili H, Rashedi E, Mahani A. Gravitational search algorithm with both attractive and repulsive forces. Soft comput 2017. [DOI: 10.1007/s00500-017-2785-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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BICA: a binary imperialist competitive algorithm and its application in CBIR systems. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0686-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Application of binary quantum-inspired gravitational search algorithm in feature subset selection. APPL INTELL 2017. [DOI: 10.1007/s10489-017-0894-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.11.018] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Zhang Y, Gong DW, Cheng J. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:64-75. [PMID: 26353379 DOI: 10.1109/tcbb.2015.2476796] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.
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Beheshti Z, Shamsuddin SM, Hasan S. Memetic binary particle swarm optimization for discrete optimization problems. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2014.12.016] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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