Feature selection algorithm for high dimensional biomedical data classification based on redundant removal

High dimensional biomedical data contain thousands of features, and accurate identification of the main features in these data can be used to classification related data. However, it is usually a large number of irrelevant or redundant features seriously influence classification accuracy. To solve this problem, a new feature selection algorithm based on redundant removal is proposed in this study. Firstly, two redundant criteria are determined by vertical relevance and horizontal relevance. Secondly, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. Finally, to evaluate the effectiveness of our proposed method, contrast experiments based on the classic feature selection algorithm are conducted using (K-nearest neighbour) KNN classifiers, and the results show that our algorithm can effectively improve the classification accuracy.

Content

Author and article information

Contributors

Bingtao Zhang

Peng Cao

Yi Zhang

Chaochao Zhang

Zhe Li

Zhidiao Qu

Xiaopeng Wang

Tao lei

Hanshu Cai

Bin Hu

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-5

Affiliations

[0001]School of Information Science and Engineering, Lanzhou University, Lanzhou, China

[0002]School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China

[0003]School of Architecture and Urban Planning, Lanzhou Jiaotong University, Lanzhou, China

[0004]College of Electronical and Information Engineering, Shaanxi University of Science and Technology, 710021, Xi’an, China

Article

DOI: 10.14236/ewic/HCI2018.230

SO-VID: fef12549-a000-4e69-8230-47e3cf1cf964

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.230

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

A. AsuncionD. J. Newman 2007 UCI machine learning repository Online available: http://archive.ics.uci.edu/ml/
K. ChrysostomouS. Y. ChenX. Liu 2017 Combining multiple classifiers for wrapper feature selection International Journal of Data Mining Modelling & Management 1 91 102
Y. L. ChuangH. W. ChangC. J. TuC. H. Yang 2008 Improved binary PSO for feature selection using gene expression data Computational Biology and Chemistry 32 29 38
P. A. EstevezM. TesmerC. A. PerezJ. M. Zurada 2009 Normalized Mutual Information Feature Selection IEEE Transactions on Neural Networks 20 189 201
E. HancerB. XueM. Zhang 2018 Differential evolution for filter feature selection based on information theory and feature ranking Knowledge-Based Systems 140 103 119
B. HuY. DaiY. SuX. W. ZhangC. S. MaoJ. ChenL. X. Xu 2016 Feature Selection for Optimized High-dimensional Biomedical Data using the Improved Shuffed Frog Leaping Algorithm IEEE/ACM Transactions on Computational Biology & Bioinformatics 99 1 10
H. H. JiangB. HuZ. Y. LiuL. H. YanT. Y. WangF. LiuH. Y. KangX. Y. Li 2017 Investigation of different speech types and emotions for detecting depression using different classifiers Speech Communication 90 39 46
G. H. JohnR. KohaviK. Pfleger 1994 Irrelevant Features and the Subset Selection Problem Machine Learning Proceedings 1994 121 129
I. Kononenko 1994 Estimating attributes: analysis and extensions of RELIEF European Conference on Machine Learning on Machine Learning 23 171 182
K. LeeZ. ManD. WangZ. Cao 2013 Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis Neural Computing and Applications 22 457 468
T. LeiX. JiaY. ZhangL. F. HeH. Y. MengA. K. Nandi 2018 Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering IEEE Transactions on Fuzzy Systems 27 1 15
J. LiH. Liu 2004 Kent Ridge Biomedical Data Set Repository School of Computer Engineering, Nanyang Technological University, Singapore Online available: http://datam.i2r.astar.edu.sg/datasets/krbd/index.html
X. W. LiJ. ZhuangB. HuJ. ZhuN. ZhongM. LiZ. J. DingJ. YangL. ZhangL. FengD. Majoe 2017 A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering Complexity 22 1 11
X. LiB. HuS. SunH.S. Cai 2016 EEG-based mild depressive detection using feature selection methods and classifiers Computer Methods and Programs in Biomedicine 36 151 161
M. M. MafarjaS. Mirjalili 2018 Whale Optimization Approaches for Wrapper Feature Selection Applied Soft Computing 62 441 453
H. PengF. LongC. Ding 2005 Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans Pattern Anal Mach Intell 27 1226 1238
Y. SaeysI. InzaP. Larrañaga 2007 A review of feature selection techniques in bioinformatics Bioinformatics 23 2507 2517
J. S. TamaresisJ. C. IrwinG. A. GoldfienJ. T. RabbanR. O. BurneyC. NezhatL. V. DePaoloL. C. Giudice 2014 Molecular classification of endometriosis and disease stage using high-dimensional genomic data Endocrinology 155 4986 4999
B. T. ZhangT. LeiH. LiuH.S. Cai 2018 EEG-based automatic sleep staging using ontology and weighting feature analysis Computational and Mathematical Methods in Medicine 2018 1 28
B. T. ZhangX. P. WangL. C. WangZ. L. ZhangY. L. LiH. Liu 2018 Intrusion Detection Method for MANET Based on Graph Theory Journal of Electronics and Information Technology 40 1446 1452

Comments

Comment on this article

[1] A. AsuncionD. J. Newman 2007 UCI machine learning repository Online available: http://archive.ics.uci.edu/ml/

[2] K. ChrysostomouS. Y. ChenX. Liu 2017 Combining multiple classifiers for wrapper feature selection International Journal of Data Mining Modelling & Management 1 91 102

[3] Y. L. ChuangH. W. ChangC. J. TuC. H. Yang 2008 Improved binary PSO for feature selection using gene expression data Computational Biology and Chemistry 32 29 38

[4] P. A. EstevezM. TesmerC. A. PerezJ. M. Zurada 2009 Normalized Mutual Information Feature Selection IEEE Transactions on Neural Networks 20 189 201

[5] E. HancerB. XueM. Zhang 2018 Differential evolution for filter feature selection based on information theory and feature ranking Knowledge-Based Systems 140 103 119

[6] B. HuY. DaiY. SuX. W. ZhangC. S. MaoJ. ChenL. X. Xu 2016 Feature Selection for Optimized High-dimensional Biomedical Data using the Improved Shuffed Frog Leaping Algorithm IEEE/ACM Transactions on Computational Biology & Bioinformatics 99 1 10

[7] H. H. JiangB. HuZ. Y. LiuL. H. YanT. Y. WangF. LiuH. Y. KangX. Y. Li 2017 Investigation of different speech types and emotions for detecting depression using different classifiers Speech Communication 90 39 46

[8] G. H. JohnR. KohaviK. Pfleger 1994 Irrelevant Features and the Subset Selection Problem Machine Learning Proceedings 1994 121 129

[9] I. Kononenko 1994 Estimating attributes: analysis and extensions of RELIEF European Conference on Machine Learning on Machine Learning 23 171 182

[10] K. LeeZ. ManD. WangZ. Cao 2013 Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis Neural Computing and Applications 22 457 468

[11] T. LeiX. JiaY. ZhangL. F. HeH. Y. MengA. K. Nandi 2018 Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering IEEE Transactions on Fuzzy Systems 27 1 15

[12] J. LiH. Liu 2004 Kent Ridge Biomedical Data Set Repository School of Computer Engineering, Nanyang Technological University, Singapore Online available: http://datam.i2r.astar.edu.sg/datasets/krbd/index.html

[13] X. W. LiJ. ZhuangB. HuJ. ZhuN. ZhongM. LiZ. J. DingJ. YangL. ZhangL. FengD. Majoe 2017 A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering Complexity 22 1 11

[14] X. LiB. HuS. SunH.S. Cai 2016 EEG-based mild depressive detection using feature selection methods and classifiers Computer Methods and Programs in Biomedicine 36 151 161

[15] M. M. MafarjaS. Mirjalili 2018 Whale Optimization Approaches for Wrapper Feature Selection Applied Soft Computing 62 441 453

[16] H. PengF. LongC. Ding 2005 Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans Pattern Anal Mach Intell 27 1226 1238

[17] Y. SaeysI. InzaP. Larrañaga 2007 A review of feature selection techniques in bioinformatics Bioinformatics 23 2507 2517

[18] J. S. TamaresisJ. C. IrwinG. A. GoldfienJ. T. RabbanR. O. BurneyC. NezhatL. V. DePaoloL. C. Giudice 2014 Molecular classification of endometriosis and disease stage using high-dimensional genomic data Endocrinology 155 4986 4999

[19] B. T. ZhangT. LeiH. LiuH.S. Cai 2018 EEG-based automatic sleep staging using ontology and weighting feature analysis Computational and Mathematical Methods in Medicine 2018 1 28

[20] B. T. ZhangX. P. WangL. C. WangZ. L. ZhangY. L. LiH. Liu 2018 Intrusion Detection Method for MANET Based on Graph Theory Journal of Electronics and Information Technology 40 1446 1452

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