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Evolutionary Feature Selection and Construction

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Encyclopedia of Machine Learning and Data Mining

Abstract

Representation of input data has an essential influence on the performance of machine learning systems. Evolutionary algorithms can be used to transform data representation by selecting some of the existing features (evolutionary feature selection) or constructing new features from the existing ones (evolutionary feature construction). This entry provides the rationale for both these approaches and systematizes the research and applications in this area.

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  • Arnaldo I, Krawiec K, O’Reilly U-M (2014) Multiple regression genetic programming. In: Igel C, Arnold DV, Gagne C, Popovici E, Auger A, Bacardit J, Brockhoff D, Cagnoni S, Deb K, Doerr B, Foster J, Glasmachers T, Hart E, Heywood MI,Iba H, Jacob C, Jansen T, Jin Y, Kessentini M, Knowles JD, Langdon WB, Larranaga P, Luke S, Luque G, McCall JAW, Montes de Oca MA, Motsinger-Reif A, Ong YS, Palmer M, Parsopoulos KE, Raidl G, Risi S, Ruhe G, Schaul T, Schmickl T, Sendhoff B, Stanley KO, Stuetzle T, Thierens D, Togelius J, Witt C, Zarges C (eds) GECCO ’14: proceedings of the 2014 conference on genetic and evolutionary computation, SIGEVO, Vancouver, 12–16 July. ACM, New York, pp 879–886. doi:10.1145/2576768.2598291, ISBN 978-1-4503-2662-9, http://doi.acm.org/10.1145/2576768.2598291

  • Bhanu B, Lin Y, Krawiec K (2005) Evolutionary synthesis of pattern recognition systems. Springer, New York

    MATH  Google Scholar 

  • Howard D, Roberts SC, Ryan C (2006) Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance. Pattern Recognit Lett 27(11):1275–1288

    Article  Google Scholar 

  • JaÅ›kowski W, Krawiec K, Wieloch B (2007) Knowledge reuse in genetic programming applied to visual learning. In: Thierens D et al (eds) GECCO’07: proceedings of the 9th annual conference on genetic and evolutionary computation, vol 2. ACM Press, London, pp 1790–1797

    Chapter  Google Scholar 

  • KomosiÅ„ski M, Krawiec K (2000) Evolutionary weighting of image features for diagnosing of CNS tumors. Artif Intell Med 19(1):25–38

    Article  Google Scholar 

  • Krawiec K, Bhanu B (2005) Visual learning by coevolutionary feature synthesis. IEEE Trans Syst Man Cybern Part B 35(3):409–425

    Article  Google Scholar 

  • Krawiec K, Howard D, Zhang M (2007) Overview of object detection and image analysis by means of genetic programming techniques. In: Proceedings of frontiers in the convergence of bioscience and information technologies 2007 (fbit2007), Jeju, 11–13 oct 2007. IEEE CS Press, pp 779–784

    Google Scholar 

  • Langdon W, Gustafson S, Koza J (2009) The genetic programming bibliography. http://www.cs.bham.ac.uk/~wbl/biblio/ [online]

  • Neshatian K, Zhang M (2011) Using genetic programming for context-sensitive feature scoring in classification problems. Connect Sci 23(3):183–207. doi:10.1080/09540091.2011.630065, http://www.tandfonline.com/doi/abs/10.1080/09540091.2011.630065, http://www.tandfonline.com/doi/pdf/10.1080/09540091.2011.630065

    Google Scholar 

  • Olague G, Trujillo L (2012) Interest point detection through multiobjective genetic programming. Appl Soft Comput 12(8):2566–2582. doi:10.1016/j.asoc.2012.03.058, ISSN 1568-4946, http://www.sciencedirect.com/science/article/pii/S1568494612001706

    Google Scholar 

  • Puente C, Olague G, Smith SV, Bullock SH, González-Botello MA, Hinojosa-Corona A (2009) A novel GP approach to synthesize vegetation indices for soil eros ion assessment. In: Giacobini M et al (eds) Applications of evolutionary computing. Springer, Berlin/New York, pp 375–384

    Chapter  Google Scholar 

  • Rizki MM, Zmuda MA, Tamburino LA (2002) Evolving pattern recognition systems. IEEE Trans Evolut Comput 6(6):594–609

    Article  Google Scholar 

  • Teller A, Veloso M (1997) PADO: a new learning architecture for object recognition. In: Ikeuchi K, Veloso M (eds) Symbolic visual learning. Oxford Press, New York, pp 77–112

    Google Scholar 

  • Vafaie H, Imam IF (1994) Feature selection methods: genetic algorithms vs. greedy-like search. In: Proceedings of international conference on fuzzy and intelligent control systems, Louisville, Mar 1994

    Google Scholar 

  • Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. IEEE Trans Intell Syst 13(2):44–49

    Article  Google Scholar 

  • Zhang Y, Rockett PI (2011) A generic optimising feature extraction method using multiobjective genetic programming. Appl Soft Comput 11(1):1087–1097. doi:10.1016/j.asoc.2010.02.008, ISSN 1568-4946, http://www.sciencedirect.com/science/article/B6W86-4YGHGKT-2/2/3c6f14d2e029af14747957a5a2ccfd11

    Google Scholar 

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Author information

Authors and Affiliations

  1. Poznan University of Technology, Poznan, Poland

    Krzysztof Krawiec

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  1. Krzysztof Krawiec
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Correspondence to Krzysztof Krawiec .

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Editors and Affiliations

  1. Engineering (CSE), University of New South Wales School of Computer Science &, Sydney, New South Wales, Australia

    Claude Sammut

  2. Software Engineering, Monash University School of Computer Science &, Melbourne, Victoria, Australia

    Geoffrey I. Webb

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Krawiec, K. (2016). Evolutionary Feature Selection and Construction. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_90-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_90-1

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  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-1-4899-7502-7

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