A hybrid filter-based feature selection method via hesitant fuzzy and rough sets concepts

Document Type : Original Manuscript


1 Department of computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

2 Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran


High dimensional microarray datasets are difficult to classify since they have many features with small number of
instances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improve
the classification performance of microarray datasets by selecting the significant features. Combining the concepts of
rough sets, weighted rough set, fuzzy rough set and hesitant fuzzy sets for developing an effective algorithm is the main
contribution of this paper. The mentioned method has two steps, in the first step, four discretization approaches are
applied to discretize continuous datasets and selects a primary subset of features by combining of weighted rough set
dependency degree and information gain via hesitant fuzzy aggregation approach. In the second step, a significance
measure of features (defined by fuzzy rough concepts) is employed to remove redundant features from primary set.
The Wilcoxon Signed Ranked tes (A Non-parametric statistical test) is conducted for comparing the presented method
with ten feature selection methods across seven datasets. The results of experiments show that the proposed method
is able to select a significant subset of features and it is an effective method in the literature in terms of classification
performance and simplicity.


[1] V. Bol´on-Canedo, N. S´anchez-Maro~no, A. Alonso-Betanzos, A review of feature selection methods on synthetic
data, Knowledge and information systems,
34(3) (2013), 483-519.
[2] V. Bol´on-Canedo, N. S´anchez-Marono, A. Alonso-Betanzos, J. M. Ben´ıtez, F. Herrera, A review of microarray
datasets and applied feature selection methods, Information Sciences,
282 (2014), 111-135.
[3] G. Chandrashekar, F. Sahin, A survey on feature selection methods, Computers & Electrical Engineering,
(2014), 16-28.
[4] N. Chen, Z. Xu, M. Xia, Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis,
Applied Mathematical Modelling,
37(4) (2013), 2197-2211.
[5] Y. Chen, Y. Xue, Y. Ma, F. Y, Measures of uncertainty for neighborhood rough sets, Knowledge-Based Systems,
120 (2017), 226-235.
[6] Y. Chen, Z. Zhang, J. Zheng, Y. Ma, Y. Xue, Gene selection for tumor classification using neighborhood rough sets
and entropy measures, Journal of Biomedical Informatics,
67 (2017), 59-68.
[7] B. Choi, H. Kim, W. Cha, A Comparative Study on Discretization Algorithms for Data Mining, Communications
for Statistical Applications and Methods,
18(1) (2011), 89-102.
[8] A. Chouchoulas, Q. Shen, Rough set-aided keyword reduction for text categorization, Applied Artificial Intelligence,
15(9) (2001), 843-873.
[9] J. Dai, Q. Xu, Attribute selection based on information gain ratio in fuzzy rough set theory with application to
tumor classification, Applied Soft Computing,
13(1) (2013), 211-221.
[10] M.K. Ebrahimpour, M. Eftekhari, Ensemble of feature selection methods: A hesitant fuzzy sets approach, Applied
Soft Computing,
50 (2017), 300-312.
[11] M. K. Ebrahimpour, M. Zare, M. Eftekhari, G. Aghamolaei, Occam’s razor in dimension reduction: Using reduced
row Echelon form for finding linear independent features in high dimensional microarray datasets, Engineering
Applications of Artificial Intelligence,
62 (2017), 214-221.
[12] U. M. Fayyad, K. B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning,
in: Proceedings of the International Joint Conference on Uncertainty in AI, Chambery, France,
6(1) (1993), 1022-
[13] I. Guyon, A. Elisseeff, An introduction to variable and feature selection, Journal of machine learning research,
3(1) (2003), 1157-1182.
[14] M. A. Hall, Correlation-based feature selection for machine learning, University of Waikato Hamilton, (1999),
[15] R. Jensen, Q. Shen, New approaches to fuzzy-rough feature selection, IEEE Transactions on Fuzzy Systems,
(2009), 824-838.
[16] K. Kaneiwa, A rough set approach to multiple dataset analysis, Applied Soft Computing,
11(2) (2011), 2538-2547.
[17] I. Kononenko, Estimating attributes: analysis and extensions of RELIEF, European conference on machine learning, (1994), 171-182.
[18] M. Kudo, J. Sklansky, Comparison of algorithms that select features for pattern classifiers, Pattern recognition,
33(1) (2000), 25-41.
[19] J. Liu, Q. Hu, D. Yu, A comparative study on rough set based class imbalance learning, Knowledge-Based Systems,
21(8) (2008), 753-763.
[20] J. Liu, Q. Hu, D. Yu, A weighted rough set based method developed for class imbalance learning, Information
178(4) (2008), 1235-1256.
[21] P. Maji, A Rough Hypercuboid Approach for Feature Selection in Approximation Spaces, IEEE Transactions on
Knowledge and Data Engineering,
26(1) (2014), 16-29.
[22] P. Maji, P. Garai, On fuzzy-rough attribute selection: criteria of max-dependency, max-relevance, min-redundancy,
and max-significance, Applied Soft Computing,
13(9) (2013), 3968-3980.
[23] P. Maji, S. K. Pal, Fuzzy Rough Sets for Information Measures and Selection of Relevant Genes From Microarray
Data, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics),
40(3) (2010), 741-752.
[24] P. Maji, S. Paul, Rough set based maximum relevance-maximum significance criterion and Gene selection from
microarray data, International Journal of Approximate Reasoning,
52(3) (2011), 408-426.
[25] P.E. Meyer, Information-theoretic variable selection and network inference from microarray data, Ph. D. Thesis.
Universit´e Libre de Bruxelles, (2008), 19-84.
[26] M. Moradkhani, A. Amiri, M. Javaheri, H. Safari, A hybrid algorithm for feature subset selection in highdimensional datasets using FICA and IWSSr algorithm, Applied Soft Computing,
25 (2015), 123-135.
[27] J. Moreno-Torres, J. S´aez, F. Herrera, Study on the impact of partition-induced dataset shift on
k-fold crossvalidation, IEEE Transactions on Neural Networks and Learning Systems, 23(8) (2012), 1304-1312.
[28] Z. Pawlak, Rough sets, International Journal of Parallel Programming,
11(5) (1982), 341-356.
[29] H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of max-dependency, maxrelevance, and min-redundancy, IEEE Transactions on pattern analysis and machine intelligence,
27(8) (2005),
[30] D. Sheskin, Handbook of parametric and nonparametric statistical procedures, crc Press, (2003), 225-239.
[31] A. Statnikov, I. Tsamardinos, Y. Dosbayev, C. F. Aliferis, GEMS: a system for automated cancer diagnosis and
biomarker discovery from microarray gene expression data, International journal of medical informatics,
(2005), 491-503.
[32] V. Torra, Hesitant fuzzy sets, International Journal of Intelligent Systems,
25(6) (2010), 529-539.
[33] E. Tuv, A. Borisov, G. Runger, K. Torkkola, Feature selection with ensembles, artificial variables, and redundancy
elimination, Journal of Machine Learning Research,
10(Jul) (2009), 1341-1366.
[34] H. U˘guz, A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm, Knowledge-Based Systems,
24(7) (2011), 1024-1032.
[35] C. Wang, M. Shao, Q. He, Y. Qian, Y. Qi, Feature subset selection based on fuzzy neighborhood rough sets,
Knowledge-Based Systems,
111 (2016), 173-179.
[36] X. Zhang, C. Mei, D. Chen, J. Li, Feature selection in mixed data: A method using a novel fuzzy rough set-based
information entropy, Pattern Recognition,
56 (2016), 1-15.