nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm

verfasst von : Claudio De Stefano, Francesco Fontanella, Alessandra Scotto di Freca

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In classification and clustering problems, feature selection techniques can be used to reduce the dimensionality of the data and increase the performances. However, feature selection is a challenging task, especially when hundred or thousands of features are involved. In this framework, we present a new approach for improving the performance of a filter-based genetic algorithm. The proposed approach consists of two steps: first, the available features are ranked according to a univariate evaluation function; then the search space represented by the first M features in the ranking is searched using a filter-based genetic algorithm for finding feature subsets with a high discriminative power.

Experimental results demonstrated the effectiveness of our approach in dealing with high dimensional data, both in terms of recognition rate and feature number reduction.

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

Vorheriges Kapitel Surrogate-Model Based Particle Swarm Optimisation with Local Search for Feature Selection in Classification

Nächstes Kapitel Brain Programming and the Random Search in Object Categorization

Note that the same holds also for the feature-class correlation.

Nips 2003 workshop on feature extraction and feature selection challenge (2003). http://clopinet.com/isabelle/Projects/NIPS2003

Bermejo, P., Gámez, J.A., Puerta, J.M.: Improving incremental wrapper-based subset selection via replacement and early stopping. IJPRAI 25(5), 605–625 (2011)MathSciNet

Cordella, L.P., De Stefano, C., Fontanella, F., Marrocco, C., Scotto di Freca, A.: Combining single class features for improving performance of a two stage classifier. In: 20th International Conference on Pattern Recognition (ICPR 2010), pp. 4352–4355. IEEE Computer Society (2010)

Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)CrossRef

De Stefano, C., Fontanella, F., Marrocco, C.: A GA-based feature selection algorithm for remote sensing images. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 285–294. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78761-7_29CrossRef

De Stefano, C., Fontanella, F., Maniaci, M., Scotto di Freca, A.: A method for scribe distinction in medieval manuscripts using page layout features. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6978, pp. 393–402. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24085-0_41CrossRef

Gütlein, M., Frank, E., Hall, M., Karwath, A.: Large scale attribute selection using wrappers. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009) (2009)

Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATH

Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann Publishers Inc., San Francisco (2000)

10.

Huang, J., Cai, Y., Xu, X.: A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recogn. Lett. 28(13), 1825–1844 (2007)CrossRef

11.

Lanzi, P.: Fast feature selection with genetic algorithms: a filter approach. In: IEEE International Conference on Evolutionary Computation, pp. 537–540, April 1997

12.

Lee, J.S., Oh, I.S., Moon, B.R.: Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1424–1437 (2004)CrossRef

13.

Li, R., Lu, J., Zhang, Y., Zhao, T.: Dynamic adaboost learning with feature selection based on parallel genetic algorithm for image annotation. Knowl. Based Syst. 23(3), 195–201 (2010)CrossRef

14.

Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

15.

Liu, H., Setiono, R.: Chi2: Feature selection and discretization of numeric attributes. In: ICTAI, pp. 88–91. IEEE Computer Society, Washington, DC (1995)

16.

Manimala, K., Selvi, K., Ahila, R.: Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining. Appl. Soft Comput. 11(8), 5485–5497 (2011). http://www.sciencedirect.com/science/article/pii/S1568494611001694CrossRef

17.

Ochoa, G.: Error thresholds in genetic algorithms. Evol. Comput. 14(2), 157–182 (2006)CrossRef

18.

Oreski, S., Oreski, G.: Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst. Appl. 41(4, Part 2), 2052–2064 (2014)CrossRef

19.

Spolaôr, N., Lorena, A.C., Lee, H.D.: Multi-objective genetic algorithm evaluation in feature selection. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 462–476. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19893-9_32CrossRef

20.

Tan, F., Fu, X., Zhang, Y., Bourgeois, A.G.: A genetic algorithm-based method for feature subset selection. Soft Comput. 12(2), 111–120 (2007)CrossRef

21.

Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)CrossRef

22.

Yusta, S.C.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recogn. Lett. 30(5), 525–534 (2009)CrossRef

23.

Zhai, Y., Ong, Y.S., Tsang, I.: The emerging “big dimensionality”. IEEE Comput. Intell. Mag. 9(3), 14–26 (2014)CrossRef

Titel: Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm
verfasst von: Claudio De Stefano
Francesco Fontanella
Alessandra Scotto di Freca
Verlag: Springer International Publishing
Buch: Applications of Evolutionary Computation
Print ISBN: 978-3-319-55848-6

Electronic ISBN: 978-3-319-55849-3

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-55849-3_33

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"