Soft Computing

, Volume 21, Issue 23, pp 7141–7157 | Cite as

A new multi-colony fairness algorithm for feature selection

Methodologies and Application


As the world gradually transforms from an information world to a data-driven world, areas of pattern recognition and data mining are facing more and more challenges. The process of feature subset selection becomes a necessary part of big data pattern recognition due to the data with explosive growth. Inspired by the behavior of grabbing resources in animals, this paper adds personal grabbing-resource behavior into the model of resource allocation transformed from the model of feature selection. Multi-colony fairness algorithm (MCFA) is proposed to deal with grabbing-resource behaviors in order to obtain a better distribution scheme (i.e., to obtain a better feature subset). The algorithm effectively fuses strategies of the random search and the heuristic search. In addition, it combines methods of filter and wrapper so as to reduce the amount of calculation while improving classification accuracies. The convergence and the effectiveness of the proposed algorithm are verified both from mathematical and experimental aspects. MCFA is compared with other four classic feature selection algorithms such as sequential forward selection, sequential backward selection, sequential floating forward selection, and sequential floating backward selection and three mainstream feature selection algorithms such as relevance–redundancy feature selection, minimal redundancy–maximal relevance, and ReliefF. The comparison results show that the proposed algorithm can obtain better feature subsets both in the aspects of feature subset length which is defined as the number of features in a feature subset and the classification accuracy. The two aspects indicate the efficiency and the effectiveness of the proposed algorithm.


Feature selection Multi-colony fairness algorithm (MCFA) Resource allocation Grabbing-resource behavior 



This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472139 and 61462073, the Software and Integrated Circuit Industry Development Special Funds of Shanghai Economic and Information Commission under Grant No. 140304.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Averell L, Heathcote A (2011) The form of the forgetting curve and the fate of memories. J Math Psychol 55(1):25–35CrossRefMATHMathSciNetGoogle Scholar
  2. Azar AT, Elshazly HI, Hassanien AE et al (2014) A random forest classifier for lymph diseases. Comput Methods Programs Biomed 113(2):465–473CrossRefGoogle Scholar
  3. Bouatmane S, Roula MA, Bouridane A et al (2011) Round-robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery. Mach Vis Appl 22:865–878CrossRefGoogle Scholar
  4. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MATHMathSciNetGoogle Scholar
  5. Feng X, Yang T, Li S (2015) Network behavior-oriented CDN cache allocation strategy. Comput Sci 42:156–161Google Scholar
  6. Gan JQ, Hasan BAS, Tsui CSL (2014) A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space. Int J Mach Learn Cybern 5:413–423CrossRefGoogle Scholar
  7. Garcia S, Molina D, Lozano M et al (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the CEC2005 special session on real parameter optimization. J Heuristics 15:617–644CrossRefMATHGoogle Scholar
  8. Glten A (2013) Genetic algorithm wrapped bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases. Digit Signal Proc 23(1):230–237CrossRefMathSciNetGoogle Scholar
  9. Guyon I (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATHGoogle Scholar
  10. Han XH, Chang XM, Quan L et al (2014) Feature subset selection by gravitational search algorithm optimization. Inf Sci 281:128–146CrossRefMathSciNetGoogle Scholar
  11. Herzfeld DJ, Vaswani PA, Marko MK et al (2014) A memory of errors in sensorimotor learning. Science 345(6202):1349–1353CrossRefGoogle Scholar
  12. Juanying X, Weixin X (2014) Several feature selection algorithms based on the discernibility of a feature subset and support vector machines. Chin J Comput 37(8):1704–1718Google Scholar
  13. Linksvayer T (2014) Evolutionary biology: Survival of the fittest group. Nature 514(7522):308–309CrossRefGoogle Scholar
  14. Mar T, Zaunseder S, Martinez JP et al (2011) Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 58(8):2168–2177CrossRefGoogle Scholar
  15. Mersch DP, Crespi A, Keller L (2013) Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340(6136):1090–1093CrossRefGoogle Scholar
  16. Moradi P, Rostami M (2015) Integration of graph clustering with ant colony optimization for feature selection. Knowl Based Syst 84:144–161CrossRefGoogle Scholar
  17. Nemati S, Basiri ME (2011) Text-independent speaker verification using ant colony optimization-based selected features. Expert Syst Appl 38(1):620–630CrossRefGoogle Scholar
  18. Parkka J, Ermes M, van Gils M (2010) Automatic feature selection and classification of physical and mental load using data from wearable sensors. IEEE, WashingtonCrossRefGoogle Scholar
  19. Peng H, Yinlian F, Liu J et al (2013) Optimal gene subset selection using the modified SFFS algorithm for tumor classification. Neural Comput Appl 23:1531–1538CrossRefGoogle Scholar
  20. Peter C, Jessica JK (2008) The interaction between predation and competition. Nature 456(7219):235–238CrossRefGoogle Scholar
  21. Uzer MS, Inan O, Yilmaz N (2013) A hybrid breast cancer detection system via neural network and feature selection based on sbs, sfs and pca. Neural Comput Appl 23:719–728CrossRefGoogle Scholar
  22. Vergara JR, Estevez PA (2014) A review of feature selection methods based on mutual information. Neural Comput Appl 24:175–186CrossRefGoogle Scholar
  23. Xiaofeng M, Yong L, Jianhua Z (2013) Social computing in the era of big data: opportunities and challenges. J Comput Res Dev 50(12):2483–2491Google Scholar
  24. Xie J, Lei J, Xie W (2013) Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases. Health Inf Sci Syst 1:1–14CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringEast China University of Science and TechnologyShanghaiPeople’s Republic of China

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