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2018 | OriginalPaper | Chapter

Genetic Algorithm Based Hybrid Attribute Selection Using Customized Fitness Function

Authors : C. Arunkumar, S. Ramakrishnan, Siva Sai Dheeraj

Published in: Computational Vision and Bio Inspired Computing

Publisher: Springer International Publishing

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Abstract

Attribute selection is an important step in the analysis of gene expression for cancer or illnesses in general. The huge dimensionality of gene expression data that includes many insignificant and redundant genes reduces the classification accuracy. In this study, we propose a hybrid attribute selection method to identify the small set of the most significant genes associated with the cause of cancer. The proposed method integrates the advantages of filter and a wrapper to perform attribute selection by devising a customized fitness function for the genetic algorithm. Three data sets are used that includes leukemia, CNS and colon cancer. Results of our technique are compared with the other standard techniques available in literature. The proposed hybrid approach produces comparably better accuracy than the standard implementation of the genetic algorithm.

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Literature
1.
Eseyin, O.A., Satt, M.A., Rathore H.A.: A review of the pharmacological and biological activities of the aerial parts of Telfairia occidentalis. Trop. J. Pharm. Res. 13(10), 1761–1769 (2014)
2.
Latkowskia, T., Osowskia, S.: Data mining for feature selection in gene expression autism data. Expert Syst. Appl. 42(2), 864–872 (2015)CrossRef
3.
Huy, P.Q., Ngom, A., Rueda, L.: PAFS—an efficient method for classifier-specific feature selection. In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI) (2016)
4.
Pashaei, E., Ozen, M., Aydin, N.: A novel gene selection algorithm for cancer identification based on random forest and particle swarm optimization. In: Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (2015)
5.
Hoseini, E., Mansoori, E.G.: Selecting discriminative features in social media data: an unsupervised approach. Neurocomputing 205(12), 463–471 (2016)CrossRef
6.
Saleha, A.I., Rabiea, A.H., Abo-Al-Ez, K.M.: A data mining based load forecasting strategy for smart electrical grids. Adv. Eng. Inform. 30(3), 422–448 (2016)CrossRef
7.
Dai, J., Qing, X.: Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumour classification. Appl. Soft Comput. 13, 211–221 (2013)CrossRef
8.
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)CrossRef
9.
Sreepada, R.S., Vipsita, S., Mohapatra, P.: An efficient approach for microarray data classification using filter wrapper hybrid approach. In: Proceedings of IEEE International Advance Computing Conference (IACC) (2015)
10.
Das, A.: Digital communication-principles and system modelling. ISBN 978-3-642-12743-4 (2010)
11.
Ye, S., Chen, Y., Hu, T.: Evolutionary algorithmic deployment of radio beacons for indoor positioning. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC) (2016)
12.
Hsu, H.-H., Hsieh, C.-W., Ming-Da, L.: Hybrid feature selection by combining filters and wrappers. Expert Syst. Appl. 38(7), 8144–8150 (2011)CrossRef
13.
Oluleye, B., Leisa, A., Leng, J., Dean, D.: A Genetic Algorithm-Based Feature Selection. Int. J. Electr. Commun. Comput. Eng. 5(4), 899–905 (2014)
14.
Oluleye, B., Leisa, A., Leng, J., Dean, D.: Zernike moments and genetic algorithm: tutorial and application. Br. J. Math. Comput. Sci. 4(15), 2217–2236 (2014)CrossRef
15.
Arunkumar, C., Ramakrishnan, S.: Hybrid information gain based fuzzy roughset feature selection in cancer microarray data. In: Proceedings of IEEE International Conference on Innovations in Power and Advanced Computing Technologies, Vellore Institute of Technology, Vellore, India, 21–22 April 2017
16.
Arunkumar, C., Sooraj, M., Ramakrishnan, S.: Finding expressed genes using genetic algorithm and extreme learning machines. In: Proceedings of IEEE International Conference on Advanced Computing and Communication Systems, Sri Eshwar College of Engineering, Coimbatore, India, 6–7 Jan 2017
Metadata
Title
Genetic Algorithm Based Hybrid Attribute Selection Using Customized Fitness Function
Authors
C. Arunkumar
S. Ramakrishnan
Siva Sai Dheeraj
Copyright Year
2018
Book
Computational Vision and Bio Inspired Computing

Print ISBN: 978-3-319-71766-1

Electronic ISBN: 978-3-319-71767-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-71767-8_1