Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection (original) (raw)

• Alia OM, Mandava R, Ramachandram D, Aziz ME (2009) Dynamic fuzzy clustering using harmony search with application to image segmentation. In: IEEE international symposium on signal processing and information technology (ISSPIT), pp 538–543
• Awadallah MA, Khader AT, Azmi Al-Betar M, Bolaji AL (2013) Global best Harmony Search with a new pitch adjustment designed for Nurse Rostering. J King Saud Univ Comput Inf Sci 25(2):145–162
  Google Scholar
• Bao Y, Hu Z, Xiong T (2013) A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 117:98–106
  Article Google Scholar
• Bermejo P, Gomez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recogn Lett 32(5):701–711
  Article Google Scholar
• Bonilla Huerta EB, Duval B, Hao JK (2006) A hybrid GA/SVM approach for gene selection and classification of microarray data. In: Rothlanf F et al (eds) EvoWorkshops 2006, LNCS, vol 3907, pp 34–44
• Boughaci D, Benhamou B, Drias H (2010) Local Search Methods for the optimal winner determination problem. J Math Model Algorithms (Springer) 9(2):165–180
  Article MathSciNet Google Scholar
• Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, Belmont
  MATH Google Scholar
• Campbell C, Ying Y (2011) Learning with support vector machines. Morgan and Claypool, San Rafael
  MATH Google Scholar
• Chang CC, Lin CJ (2012) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html. Accessed 29 March 2014
• Dia R, Shen Q (2012) Feature selection with harmony search. IEEE Trans Syst Man Cybern B 42(6):1509–1523
  Article Google Scholar
• Frank E, Witten IH (1998) Generating accurate rule sets without global optimization. In: Shavlik J (ed) Proceedings of the fifteenth international conference machine learning (ICML 98), pp 144–151
• Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163
  Article MATH Google Scholar
• Gao XZ, Wang X, Zenger K (2013) A memetic-inspired harmony search method in optimal wind generator design. In: International journal of machine learning and cybernetics. Springer, Berlin
• Geem ZW (2007) Harmony search algorithm for solving Sudoku. Knowl Based Intell Inf Eng Syst 4692:371–378
  Google Scholar
• Geem ZW (2009) Harmony search algorithms for structural design optimization. Springer, New York
  Book Google Scholar
• Geem ZW, Choi JY (2007) Music composition using harmony search algorithm. In: Applications of evolutionary computing. Springer, Berlin, pp 593–600
• Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68
  Article Google Scholar
• Hadwan M, Ayob M, Sabar NR, Qu R (2013) A harmony search algorithm for nurse rostering problems. Inf Sci 233:126–140
  Article MathSciNet Google Scholar
• Hamel L (2009) Knowledge discovery with support vector machines. Wiley, Canada
• Han J, Kamber M (2006) Data mining concepts and techniques, 2nd edn. Morgan Kaufmann, San Francisco
  MATH Google Scholar
• Hannah IH, Bagyamathi M, Azar TA (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl. doi:10.1007/s00521-015-1840-0
• Hertz JA, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley Publishing Company, Inc., Redwood City
  Google Scholar
• Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. Accessed 29 March 2014
• John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence. Morgan Kaufmann, San Mateo, pp 338–345
• Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. The MIT Press, London
  MATH Google Scholar
• Kohavi R, John G (1996) Wrappers for feature subset selection. Artif intell 97(1–2):273–324 (special issue on relevance)
• Krishnaveni V, Arumugam G (2013) Harmony search based wrapper feature selection method for 1-nearest neighbour classifier. In: Proceedings of the international conference on pattern recognition, informatics and mobile engineering (PRIME)
• Kumar V, Chhabra JK, Kumar D (2014) Parameter adaptive harmony search for unimodal and multimodal optimization problems. J Comput Sci 5(2):144–155. doi:10.1016/j.jocs.2013.12.001
  Article MathSciNet Google Scholar
• Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3922
  Article MATH Google Scholar
• Lessmann S, Stahlbock R, Crone SF (2006) Genetic algorithms for support vector machine model selection. In: Proceedings of the international joint conference on neural networks, IJCNN 2006, part of the IEEE world congress on computational intelligence, WCCI 2006. IEEE, Vancouver, pp 3063–3069
• Li Y, Tong Y, Bai B, Zhang Y (2007) An improved particle swarm optimization for SVM training. In: Third international conference on natural computation (ICNC 2007), pp 611–615
• Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579
  MathSciNet MATH Google Scholar
• Nekkaa M, Boughaci D (2012) Improving support vector machine using a stochastic local search for classification in data mining. In: Proceedings of ICONIP 2012, Part II, LNCS proceedings, vol 7664, pp 168–176
• Nekkaa M, Boughaci D (2014) Stochastic local search versus genetic algorithm for feature selection. In: Proceedings of APMOD CONFERENCE 2014: international conference on applied mathematical optimization and modelling 2014
• Nekkaa M, Boughaci D (2015) A memetic algorithm with support vector machine for feature selection and classification. Memetic Comput 7:59–73. doi:10.1007/s12293-015-0153-2
  Article Google Scholar
• Nekooei K, Farsangi MM, Nezamabadi-Pour H, Lee KY (2013) An improved multi-objective harmony search for optimal placement of DGs in distribution systems. IEEE Trans Smart Grid 4(1):557–567
  Article Google Scholar
• Panchal A (2009) Harmony search in therapeutic medical physics. In: Geem ZW (ed) Music-inspired harmony search algorithm. Springer, Hiedelberg, pp 189–203
• Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
  Google Scholar
• Rao R, Savsani V, Vakharia D (2012) Teaching learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183:1–15
  Article MathSciNet Google Scholar
• Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36:8616–8630
  Article Google Scholar
• Tay FEH, Cao LJ (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317
  Article Google Scholar
• Vapnik V (1998) Statistical learning theory. Wiley, New York
  MATH Google Scholar
• Vapnik V (1995) The Natural of Statistical Learning theory. Springer, New York
  Book MATH Google Scholar
• Waikato environment for knowledge analysis (WEKA), version 3.6. The University of Waikato, Hamilton. http://www.cs.waikato.ac.nz/ml/weka/downloading.html. Accessed 29 March 2014
• Yadav P, Kumar R, Panda S, Chang C (2012) An intelligent tuned harmony search algorithm for optimisation. Inf Sci 196:47–72
  Article Google Scholar
• Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88
  Article MathSciNet Google Scholar