Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm
Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensi...
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| Published in | Interdisciplinary sciences : computational life sciences Vol. 12; no. 3; pp. 288 - 301 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2020
Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose. |
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| AbstractList | Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose. Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose.Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose. |
| Author | Yan, Chaokun Luo, Junwei Wang, Jianlin Zhang, Ge Hou, Jincui |
| Author_xml | – sequence: 1 givenname: Ge surname: Zhang fullname: Zhang, Ge organization: School of Computer and Information Engineering, Henan University, Henan Engineering Research Center of Intelligent Technology and Application, Henan University – sequence: 2 givenname: Jincui surname: Hou fullname: Hou, Jincui organization: School of Computer and Information Engineering, Henan University – sequence: 3 givenname: Jianlin surname: Wang fullname: Wang, Jianlin organization: School of Computer and Information Engineering, Henan University, Henan Engineering Research Center of Intelligent Technology and Application, Henan University – sequence: 4 givenname: Chaokun orcidid: 0000-0002-6246-7242 surname: Yan fullname: Yan, Chaokun email: ckyan@henu.edu.cn organization: School of Computer and Information Engineering, Henan University, Henan Engineering Research Center of Intelligent Technology and Application, Henan University – sequence: 5 givenname: Junwei surname: Luo fullname: Luo, Junwei email: luojunwei@hpu.edu.cn organization: College of Computer Science and Technology, Henan Polytechnic University |
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| Title | Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm |
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