Soft Computing

, Volume 21, Issue 24, pp 7543–7569 | Cite as

Attribute reduction on real-valued data in rough set theory using hybrid artificial bee colony: extended FTSBPSD algorithm

Methodologies and Application
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Abstract

Discretization and attribute reduction are two preprocessing steps for most of the induction algorithms. Discretization before attribute reduction will result in high computation cost as many irrelevant and redundant attributes need to be discretized. Attribute reduction before discretization may result in over-fitting of the data leading to low performance of the induction algorithm. In this paper, we have proposed a hybrid algorithm using artificial bee colony (ABC) algorithm and extended forward tentative selection with backward propagation of selection decision (EFTSBPSD) algorithm for attribute reduction on real-valued data in rough set theory (RST). Based on the principle of indiscernibility, the hybrid ABC–EFTSBPSD algorithm performs discretization and attribute reduction together. The hybrid ABC–EFTSBPSD algorithm takes as input the decision system consisting of real-valued attributes and determines a near optimal set of irreducible cuts. Here, optimality of the set of irreducible cuts is defined in terms of the cardinality of the set of irreducible cuts. Reduct is obtained from the determined approximate optimal set of irreducible cuts by extracting the attributes corresponding to the cuts in the obtained set of irreducible cuts. The proposed hybrid algorithm is tested on various data sets from University of California Machine Learning Repository. Experimental results obtained by the proposed hybrid algorithm are compared with those obtained by the Q-MDRA, ACO-RST and IMCVR algorithms described in the literature and found to give better classification accuracy when tested using (1) C4.5 classifier and (2) SVM classifier. The proposed hybrid algorithm has also shown reduced length of the reduct in comparison with the results obtained by Q-MDRA, ACO-RST and IMCVR algorithms.

Keywords

Rough set theory Indiscernibility Boolean reasoning Discretization Attribute reduction Artificial bee colony algorithm FTSBPSD algorithm 

References

  1. Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. Mult Valued Logic Soft Comput 17:255–287Google Scholar
  2. Bhatt RB, Gopal M (2005) On fuzzy-rough sets approach to feature selection. Pattern Recognit Lett 26(7):965–975CrossRefGoogle Scholar
  3. Chebrolu S, Sanjeevi SG (2015) Attribute reduction on continuous data in rough set theory using ant colony optimization metaheuristic. In: Proceedings of the third international symposium on women in computing and informatics, WCI ’15, pp 17–24, ACM, New York, 2015Google Scholar
  4. Chebrolu S, Sanjeevi SG (2015) Forward tentative selection with backward propagation of selection decision algorithm for attribute reduction in rough set theory. Int J Reason Based Intell Syst 7(3/4):221–243Google Scholar
  5. Cornelis C, Jensen R, Hurtado G, Ślȩzak D (2010) Attribute selection with fuzzy decision reducts. Inf Sci 180(2):209–224CrossRefMATHMathSciNetGoogle Scholar
  6. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines: and other kernel-based learning methods. Cambridge University Press, New YorkCrossRefMATHGoogle Scholar
  7. Dai J, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211–221CrossRefGoogle Scholar
  8. Friedman M (1940) A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann Math Stat 11(1):86–92CrossRefMATHMathSciNetGoogle Scholar
  9. Gao KZ, Suganthan PN, Chua TJ, Chong CS, Cai TX, Pan QK (2015) A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Syst Appl 42(21):7652–7663CrossRefGoogle Scholar
  10. Guan Y-Y, Wang H-K, Wang Y, Yang F (2009) Attribute reduction and optimal decision rules acquisition for continuous valued information systems. Inf Sci 179(17):2974–2984CrossRefMATHMathSciNetGoogle Scholar
  11. Hu Q, Liu J, Yu D (2008) Mixed feature selection based on granulation and approximation. Knowl Based Syst 21(4):294–304CrossRefGoogle Scholar
  12. Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594CrossRefMATHMathSciNetGoogle Scholar
  13. IBM Corp (2013) IBM SPSS Statistics for Windows, Version 22.0. IBM Corp, ArmonkGoogle Scholar
  14. Jensen R, Shen Q (2004) Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets Syst 141(3):469–485CrossRefMATHMathSciNetGoogle Scholar
  15. Jia X, Liao W, Tang Z, Shang L (2013) Minimum cost attribute reduction in decision-theoretic rough set models. Inf Sci 219:151–167CrossRefMATHMathSciNetGoogle Scholar
  16. Jiang F, Sui Y (2015) A novel approach for discretization of continuous attributes in rough set theory. Knowl Based Syst 73:324–334CrossRefGoogle Scholar
  17. Jun Z, Zhou Y (2009) New heuristic method for data discretization based on rough set theory. J China Univ Posts Telecommun 16(6):113–120CrossRefGoogle Scholar
  18. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, October 2005Google Scholar
  19. Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5(3):6915CrossRefGoogle Scholar
  20. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MATHMathSciNetGoogle Scholar
  21. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRefGoogle Scholar
  22. Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Torra V, Narukawa Y, Yoshida Y (eds) Modeling decisions for artificial intelligence: 4th international conference, MDAI 2007, Kitakyushu, Japan, August 16–18, 2007. Proceedings, pp 318–329Google Scholar
  23. Kent ridge bio-medical data set repository. http://sdmc.lit.org.sg/gedatasets/datasets.html
  24. Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Pal SK, Skowron A (eds) Rough fuzzy hybridization: a new trend in decision-making. Springer, Singapore, pp 3–98Google Scholar
  25. Li M, Deng SB, Feng S, Fan J (2011) An effective discretization based on class-attribute coherence maximization. Pattern Recognit Lett 32(15):1962–1973CrossRefGoogle Scholar
  26. Li M, Shang C, Feng S, Fan J (2014) Quick attribute reduction in inconsistent decision tables. Inf Sci 254:155–180CrossRefMATHMathSciNetGoogle Scholar
  27. Lichman M (2013) UCI machine learning repository. School of Information and Computer Sciences, University of California, Irvine. http://archive.ics.uci.edu/ml
  28. Mac Parthaláin N, Jensen R (2013) Unsupervised fuzzy-rough set-based dimensionality reduction. Inf Sci 229:106–121CrossRefMATHMathSciNetGoogle Scholar
  29. Mac Parthaláin N, Shen Q (2009) Exploring the boundary region of tolerance rough sets for feature selection. Pattern Recognit 42(5):655–667CrossRefMATHGoogle Scholar
  30. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356CrossRefMATHGoogle Scholar
  31. Pawlak Z (2002) Rough set theory and its applications. J Telecommun Inf Technol 3:7–10Google Scholar
  32. Pawlak Z, Skowron A (2007a) Rough sets and boolean reasoning. Inf Sci 177(1):41–73CrossRefMATHMathSciNetGoogle Scholar
  33. Pawlak Z, Skowron A (2007b) Rough sets: some extensions. Inf Sci 177(1):28–40CrossRefMATHMathSciNetGoogle Scholar
  34. Pawlak Z, Skowron A (2007c) Rudiments of rough sets. Inf Sci 177(1):3–27Google Scholar
  35. Pawlak Z, Grzymala-Busse J, Slowinski R, Ziarko W (1995) Rough sets. Commun ACM 38(11):88–95CrossRefGoogle Scholar
  36. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc, San FranciscoGoogle Scholar
  37. Roy A, Pal SK (2003) Fuzzy discretization of feature space for a rough set classifier. Pattern Recognit Lett 24(6):895–902CrossRefMATHGoogle Scholar
  38. Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9(2):625–631CrossRefGoogle Scholar
  39. Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Słowiński R (ed) Intelligent decision support: handbook of applications and advance of the rough sets theory, volume 11 of theory and decision library. Springer, Dordrecht, pp 331–362CrossRefGoogle Scholar
  40. Sun B, Ma W, Chen D (2014) Rough approximation of a fuzzy concept on a hybrid attribute information system and its uncertainty measure. Inf Sci 284:60–80CrossRefMATHMathSciNetGoogle Scholar
  41. Wang C, Shao M, Sun B, Qinghua H (2015) An improved attribute reduction scheme with covering based rough sets. Appl Soft Comput 26:235–243CrossRefGoogle Scholar
  42. Yao Y-Q, Mi J-S, Li Z-J (2011) Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems. Fuzzy Sets Syst 170(1):64–75CrossRefMATHMathSciNetGoogle Scholar
  43. Ye D, Chen Z (2015) A new approach to minimum attribute reduction based on discrete artificial bee colony. Soft Comput 19(7):1893–1903CrossRefGoogle Scholar
  44. Ye D, Chen Z, Ma S (2013) A novel and better fitness evaluation for rough set based minimum attribute reduction problem. Inf Sci 222:413–423CrossRefMATHMathSciNetGoogle Scholar
  45. Yong L, Wenliang H, Yunliang J, Zhiyong Z (2014) Quick attribute reduct algorithm for neighborhood rough set model. Inf Sci 271:65–81CrossRefMATHMathSciNetGoogle Scholar
  46. Zheng K, Jie H, Zhan Z, Ma J, Qi J (2014) An enhancement for heuristic attribute reduction algorithm in rough set. Expert Syst Appl 41(15):6748–6754CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology WarangalWarangalIndia

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