Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2016

01.08.2016

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

verfasst von: Messaouda Nekkaa, Dalila Boughaci

Erschienen in: Neural Processing Letters | Ausgabe 1/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Feature selection is a challenging task that has been the subject of a large amount of research, especially in relation to classification tasks. It permits to eliminate the redundant attributes and enhance the classification accuracy by keeping only the relevant attributes. In this paper, we propose a hybrid search method based on both harmony search algorithm (HSA) and stochastic local search (SLS) for feature selection in data classification. A novel probabilistic selection strategy is used in HSA–SLS to select the appropriate solutions to undergo stochastic local refinement, keeping a good compromise between exploration and exploitation. In addition, the HSA–SLS is combined with a support vector machine (SVM) classifier with optimized parameters. The proposed HSA–SLS method tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experimental results show good performance in favor of our proposed method.
Vorheriger Artikel Motor Imagery Electroencephalograph Classification Based on Optimized Support Vector Machine by Magnetic Bacteria Optimization Algorithm
Nächster Artikel Unsupervised Clustering Based an Adaptive Particle Swarm Optimization Algorithm

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
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
2.
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
3.
Bao Y, Hu Z, Xiong T (2013) A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 117:98–106CrossRef
4.
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):701–711CrossRef
5.
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
6.
Boughaci D, Benhamou B, Drias H (2010) Local Search Methods for the optimal winner determination problem. J Math Model Algorithms (Springer) 9(2):165–180MathSciNetCrossRef
7.
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, BelmontMATH
8.
Campbell C, Ying Y (2011) Learning with support vector machines. Morgan and Claypool, San RafaelMATH
9.
10.
Dia R, Shen Q (2012) Feature selection with harmony search. IEEE Trans Syst Man Cybern B 42(6):1509–1523CrossRef
11.
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
12.
Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163CrossRefMATH
13.
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
14.
Geem ZW (2007) Harmony search algorithm for solving Sudoku. Knowl Based Intell Inf Eng Syst 4692:371–378
15.
Geem ZW (2009) Harmony search algorithms for structural design optimization. Springer, New YorkCrossRef
16.
Geem ZW, Choi JY (2007) Music composition using harmony search algorithm. In: Applications of evolutionary computing. Springer, Berlin, pp 593–600
17.
Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68CrossRef
18.
Hadwan M, Ayob M, Sabar NR, Qu R (2013) A harmony search algorithm for nurse rostering problems. Inf Sci 233:126–140MathSciNetCrossRef
19.
Hamel L (2009) Knowledge discovery with support vector machines. Wiley, Canada
20.
Han J, Kamber M (2006) Data mining concepts and techniques, 2nd edn. Morgan Kaufmann, San FranciscoMATH
21.
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
22.
Hertz JA, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley Publishing Company, Inc., Redwood City
23.
24.
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
25.
Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. The MIT Press, LondonMATH
26.
Kohavi R, John G (1996) Wrappers for feature subset selection. Artif intell 97(1–2):273–324 (special issue on relevance)
27.
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)
28.
29.
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3922CrossRefMATH
30.
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
31.
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
32.
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579MathSciNetMATH
33.
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
34.
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
35.
36.
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):557–567CrossRef
37.
Panchal A (2009) Harmony search in therapeutic medical physics. In: Geem ZW (ed) Music-inspired harmony search algorithm. Springer, Hiedelberg, pp 189–203
38.
Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
39.
Rao R, Savsani V, Vakharia D (2012) Teaching learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183:1–15MathSciNetCrossRef
40.
Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36:8616–8630CrossRef
41.
Tay FEH, Cao LJ (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317CrossRef
42.
Vapnik V (1998) Statistical learning theory. Wiley, New YorkMATH
43.
44.
45.
Yadav P, Kumar R, Panda S, Chang C (2012) An intelligent tuned harmony search algorithm for optimisation. Inf Sci 196:47–72CrossRef
46.
Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88MathSciNetCrossRef
Metadaten
Titel
Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection
verfasst von
Messaouda Nekkaa
Dalila Boughaci
Publikationsdatum
01.08.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2016
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9450-5

Weitere Artikel der Ausgabe 1/2016

Neural Processing Letters 1/2016 Zur Ausgabe

OriginalPaper

A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications

OriginalPaper

An Improved Neural Network with Random Weights Using Backtracking Search Algorithm

OriginalPaper

Memcomputing Implementation of Ant Colony Optimization

OriginalPaper

Inverse Back Analysis Based on Evolutionary Neural Networks for Underground Engineering

OriginalPaper

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

OriginalPaper

Training Neural Networks with Krill Herd Algorithm

Neuer Inhalt

    Bildnachweise