Abstract
Many approaches have been tried out for feature selection, which is aimed at finding a minimal subset of the original features with predetermined targets. However, a complete search isn’t feasible for even medium-sized datasets and it has been proved that finding a minimal subset of the features is a NP-hard problem. Rough set theory is one of the effective methods to feature selection, and gravitational search algorithm (GSA), which has a flexible and well-balanced mechanism to enhance exploration and exploitation, has been successfully applied in many difficult problems. In this paper, a novel approach, called FSRG, for feature selection based on rough set and GSA is proposed, and 5 UCI datasets are used as an illustrated example. The results demonstrate that FSRG is an efficient method for feature selection.
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References
Bae C, Yeh WC, Chung YY, Liu SL (2010) Feature selection with intelligent dynamic swarm and rough set. Expert Syst Appl 37(10):7026–7032
Bhatt RB, Gopal M (2005) On fuzzy-rough sets approach to feature selection. Pattern Recogn Lett 26(7):965–975
Chen Y, Miao D, Wang R (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recogn Lett 31(3):226–233
Chen HL, Yang B, Liu J, Liu DL (2011a) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014–9022
Chen YM, Miao DQ, Wang RZ, Wu WS (2011b) A rough set approach to feature selection based on power set tree. Knowl Based Syst 24(2):275–281
Foithong S, Pinngern O, Attachoo B (2012) Feature subset selection wrapper based on mutual information and rough sets. Expert Syst Appl 39(1):574–584
Jin C, Jin S, Qin L (2012) Attribute selection method based on a hybrid BPNN and PSO algorithms. Appl Soft Comput 12(8):2147–2155
Liang JY, Wang F, Dang CG, Qian YH (2012) An efficient rough feature selection algorithm with a multi-granulation view. Int J Approx Reason 53(6):912–926
Liu H, Motoda H, Yu L (2004) A selective sampling approach to active feature selection. Artif Intell 159(1–2):49–74
Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177(1):3–27
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Rashedi E, Nezamabadi-pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745
Sun Z, Bebis G, Miller R (2004) Object detection using feature subset selection. Pattern Recogn 37(11):2165–2176
Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24(6):833–849
Thangavel K, Pethalakshm A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9(1):1–12
Wang X, Yang J, Teng XL, Xia WJ, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471
Xu Y, Li LS, Li XJ (2010) Improved LEM2 rule induction algorithm. Syst Eng Theory Pract 30(10):1841–1849. (Chinese)
Acknowledgment
This study was supported by the National Science Foundation of China under Grant 71071107 and the key project of the National Science Foundation of China under Grant 70931004.
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Wang, Hq., Niu, Zw., Liang, Lj. (2013). Feature Selection Based on Rough Set and Gravitational Search Algorithm. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40063-6_41
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DOI: https://doi.org/10.1007/978-3-642-40063-6_41
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