Neighborhood rough set reduction with fish swarm algorithm
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Feature reduction refers to the problem of deleting those input features that are less predictive of a given outcome; a problem encountered in many areas such as pattern recognition, machine learning and data mining. In particular, it has been successfully applied in tasks that involve datasets containing huge numbers of features. Rough set theory has been used as such a data set preprocessor with much success, but current methods are inadequate at solving the problem of numerical feature reduction. As the classical rough set model can just be used to evaluate categorical features, we introduce a neighborhood rough set model to deal with numerical datasets by defining a neighborhood relation. However, this method is still not enough to find the optimal subsets regularly. In this paper, we propose a new feature reduction mechanism based on fish swarm algorithm (FSA) in an attempt to polish up this. The method is then applied to the problem of finding optimal feature subsets in the neighborhood rough set reduction process. We define three foraging behaviors of fish to find the optimal subsets and a fitness function to evaluate the best solutions. We construct the neighborhood feature reduction algorithm based on FSA and design some experiments comparing with a heuristic neighborhood feature reduction method. Experimental results show that the FSA-based neighborhood reduction method is suitable to deal with numerical data and more possibility to find an optimal reduct.
KeywordsGranular computing Rough set theory Neighborhood system Feature reduction Fish swarm algorithm
This study was funded by Open Fund Project of State International S&T Cooperation Base of Networked Supporting Software (Nos. NSS1404, NSS1405), National Natural Science Foundation of China (No. 61573297), Postdoctoral Science Foundation of China (No. 2014M562306) and Natural Science Foundation of Fujian Province (No. 2015J01277).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
- Ansari E, Sadreddini MH, Sadeghi B, Alimardani Bigham F (2013) A combinatorial cooperative-tabu search feature reduction approach. Sci Iran 20(3):657–662Google Scholar
- Chen X, Sun D, Wang J, Liang J (2008) Time series forecasting based on novel support vector machine using artificial fish swarm algorithm. In: Proceedings 4th international conference on natural computation, pp 206–211Google Scholar
- Chen BJ, Shu HZ, Coatrieux G et al (2015) Color image analysis by quaternion-type moments. J Math Imaging Vis 51(1):124–144Google Scholar
- Hou ML, Wang SL, Li XL et al (2010) Neighborhood rough set reduction-based gene selection and prioritization for gene expression profile analysis and molecular cancer classification. J Biomed Biotechnol 6:1110–7243Google Scholar
- Jensen R, Shen Q (2003) Finding rough set reducts with ant colony optimization. In: Proceeding of 2003 UK workshop computational intelligence, pp 15–22Google Scholar
- Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. In: Proceedings of AAAI-92, San Jose, CA, pp 129–134Google Scholar
- Kohavi R (1994) Feature subset selection using the wrapper method: Overfitting and dynamic search space topology. In: Proceedings of AAAI fall symposium on relevance, pp 109–113Google Scholar
- Li X, Shao Z, Qian J (2002) An optimizing method based on autonomous animates: fish-swarm algorithm. Syst Eng Theory Pract 22:32–38Google Scholar
- Li X, Xue Y, Lu F, Tian G (2004) Parameter estimation method based on artificial fish school algorithm. J ShanDong Univ (Eng Sci) 34(3):84–87Google Scholar
- Li X, Lu F, Tian G, Qian J (2004) Applications of artificial fish school algorithm in combinatorial optimization problems. J ShanDong Univ (Eng Sci) 34(5):64–67Google Scholar
- Lin TY (2001) Granulation and nearest neighborhoods: rough set approach. In: Granular computing. Physica-Verlag GmbH, Heidelberg, pp 125–142Google Scholar
- Modrzejewski M (1993) Feature selection using rough sets theory. In: Proceedings of the European conference on machine learning, Vienna, Austria, pp 213–226Google Scholar
- Wen XZ, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406Google Scholar
- Yang X, Li X, Lin TY (2009) First GrC model: neighborhood systems the most general rough set models. In: GrC, pp 691–695Google Scholar
- Yuan CH, Sun XM, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13(7):60–65Google Scholar