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The Journal of Supercomputing

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06-11-2020

Feature clustering and feature discretization assisting gene selection for molecular classification using fuzzy c-means and expectation–maximization algorithm

Author: Hung-Yi Lin

Published in: The Journal of Supercomputing | Issue 6/2021

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Abstract

In this paper, a novel gene selection benefiting from feature clustering and feature discretization is developed. In large numbers of genes, unsupervised fuzzy clustering algorithm facilitates the analysis of both similarities and dissimilarities. The supervised process, adopting information gain and statistical Chi-square test, is applied to approve the relevant gene clusters. Then, expectation–maximization algorithm discretizes the candidate genes and helps to recognize distinguishability. In our previously proposed selection criterion, we finalized gene selection and generated the gene subsets for molecular classification. For high-dimensional datasets congested with erroneous or ambiguous information, the current scheme is particularly suitable in its own right. The efficiency and effectiveness are verified by our experimental results.

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Alonso-Betanzos A, Bolón-Canedo V, Morán-Fernández L, Sánchez-Maroño N (2019) A review of microarray datasets: where to find them and specific characteristics. In: Microarray bioinformatics, Humana, New York, NY, pp 65–85

Dessì N, Pes B (2015) Similarity of feature selection methods: an empirical study across data intensive classification tasks. Expert Syst Appl 42(10):4632–4642CrossRef

Mohapatra P, Chakravarty S, Dash PK (2016) Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system. Swarm Evol Comput 28:144–160CrossRef

Morán-Fernández L, Bolón-Canedo V, Alonso-Betanzos A (2017) Can classification performance be predicted by complexity measures? A study using microarray data. Knowl Inf Syst 51(3):1067–1090CrossRef

Chen J, Stern M, Wainwright MJ, Jordan MI (2017) Kernel feature selection via conditional covariance minimization. Preprint arXiv:1707.01164

Liu T, Tao D (2016) Classification with noisy labels by importance reweighting. IEEE Trans Pattern Anal Mach Intell 38(3):447–461MathSciNetCrossRef

Novaković J (2016) Toward optimal feature selection using ranking methods and classification algorithms. Yugoslav J Oper Res 21(1)

Song X, Zhang J, Han Y, Jiang J (2016) Semi-supervised feature selection via hierarchical regression for web image classification. Multimedia Syst 22(1):41–49CrossRef

Golay J, Kanevski M (2017) Unsupervised feature selection based on the morisita index for hyperspectral images. In: EGU General Assembly Conference Abstracts, vol 19, p 14396

10.

Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32:112–123CrossRef

11.

Wang S, Pedrycz W, Zhu Q, Zhu W (2015) Unsupervised feature selection via maximum projection and minimum redundancy. Knowl-Based Syst 75:19–29CrossRef

12.

Wang S, Wang H (2017) Unsupervised feature selection via low-rank approximation and structure learning. Knowl-Based Syst 124:70–79CrossRef

13.

Zhou W, Wu C, Yi Y, Luo G (2017) Structure preserving nonnegative feature self-representation for unsupervised feature selection. IEEE Access

14.

Naghieh E, Peng Y (2009) Microarray gene expression data mining: clustering analysis review

15.

Au WH, Chan KC, Wong AK, Wang Y (2005) Attribute clustering for grouping, selection, and classification of gene expression data. IEEE/ACM Trans Comput Biol Bioinf 2(2):83–101CrossRef

16.

Jiang D, Tang C, Zhang A (2004) Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 16(11):1370–1386CrossRef

17.

Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp 94–105

18.

Min E, Guo X, Liu Q, Zhang G, Cui J, Long J (2018) A survey of clustering with deep learning: from the perspective of network architecture. IEEE Access 6:39501–39514CrossRef

19.

Ahmad A, Khan SS (2019) Survey of state-of-the-art mixed data clustering algorithms. IEEE Access 7:31883–31902CrossRef

20.

Mittal M, Goyal LM, Hemanth DJ, Sethi JK (2019) Clustering approaches for high-dimensional databases: a review. Wiley Interdiscip Rev Data Min Knowl Discov 9(3):e1300CrossRef

21.

Feng J, Jiao L, Liu F, Sun T, Zhang X (2016) Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recogn 51:295–309CrossRef

22.

Lin HY (2013) Feature selection based on cluster and variability analyses for ordinal multi-class classification problems. Knowl-Based Syst 37:94–104CrossRef

23.

Stańczyk U, Jain LC (eds) (2015) Feature selection for data and pattern recognition. Springer, BerlinMATH

24.

Battiti R (1994) Using mutual information for selecting features in supervised neural net learning”. IEEE Trans Neural Netw 5(4):537–550CrossRef

25.

Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATH

26.

Kwak N, Choi C-H (2002) Input feature selection for classification problems. IEEE Trans Neural Netw 3(1):143–159CrossRef

27.

Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRef

28.

Bolón-Canedo V, Sánchez-Marono N, Alonso-Betanzos A, Benítez JM, Herrera F (2014) A review of microarray datasets and applied feature selection methods. Inf Sci 282:111–135CrossRef

29.

Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Feature selection in DNA microarray classification. In: Feature selection for high-dimensional data, Springer International Publishing, Springer, Cham, pp 61–94

30.

Canul-Reich J, Hall LO, Goldgof DB, Korecki JN, Eschrich S (2012) Iterative feature perturbation as a gene selector for microarray data. Int J Pattern Recognit Artif Intell 26(05):1260003MathSciNetCrossRef

31.

Li J, Liu H, Ng SK, Wong L (2003) Discovery of significant rules for classifying cancer diagnosis data. Bioinformatics 19(suppl_2):ii93–ii102

32.

Sharma A, Imoto S, Miyano S (2012) A top-r feature selection algorithm for microarray gene expression data. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 9(3):754–764CrossRef

33.

Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906–914CrossRef

34.

Reddy SVG, Reddy KT, Kumari VV, Varma KV (2014) An SVM based approach to breast cancer classification using RBF and polynomial kernel functions with varying arguments. Int J Comput Sci Inf Technol 5(4):5901–5904

35.

Kumar M, Rath SK (2015) Classification of microarray using MapReduce based proximal support vector machine classifier. Knowl-Based Syst 89:584–602CrossRef

36.

Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Bloomfield CD (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537CrossRef

37.

Medjahed SA, Saadi TA, Benyettou A (2013) Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. Int J Comput Appl 62(1)

38.

Alipanahi B, Delong A, Weirauch M T, Frey BJ (2015) Predicting the sequence specificities of DNA- and RNA- binding proteins by deep learning. Nat Biotechnol 33(8):831–838CrossRef

39.

Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12(10):931–934CrossRef

40.

Ahn T, Goo T, Lee CH, Kim S, Han K, Park S, Park T (2018) Deep learning-based identification of cancer or normal tissue using gene expression data. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1748–1752

41.

Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, DordrechtMATHCrossRef

42.

Pakhira MK, Bandyopadhyay S, Maulik U (2004) Validity index for crisp and fuzzy clusters. Pattern Recogn 37(3):487–501MATHCrossRef

43.

Lin HY (2016) Gene discretization based on EM clustering and adaptive sequential forward gene selection for molecular classification. Appl Soft Comput 48:683–690CrossRef

44.

Lin HY (2018) Reduced gene subset selection based on discrimination power boosting for molecular classification. Knowl-Based Syst 142:181–191CrossRef

45.

Feature Selection Datasets at Arizona State University (2018) http://featureselection.asu.edu/datasets.php

46.

Broad Institute (2018) Cancer Program Data Sets. http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi

47.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4412

48.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4115

49.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5509

50.

Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Comput 13(3):637–649MATHCrossRef

Title: Feature clustering and feature discretization assisting gene selection for molecular classification using fuzzy c-means and expectation–maximization algorithm
Author: Hung-Yi Lin
Publication date: 06-11-2020
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 6/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03480-y

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