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Instance Selection Methods and Resampling Techniques for Dissimilarity Representation with Imbalanced Data Sets

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Pattern Recognition - Applications and Methods

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 204))

Abstract

In the dissimilarity representation approach, the dimension reduction of the dissimilarity space is addressed by using instance selection methods. Several studies have shown that these methods work well on small data sets. Also, the uniformity of the instances distribution can be obtained when the classes are evenly spread and balanced. However, many real-world problems are characterized by an imbalanced class distribution. In this paper, we address the problem of instance selection for constructing the dissimilarity space in the imbalanced data context. Class imbalance is handled by resampling the data set, whereas instance selection is applied to find a small representation set. Experimental results demonstrate the significance of the joint use of resampling techniques and instance selection methods to improve the performance of classifiers trained on dissimilarity representation.

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Authors and Affiliations

  1. Institute of New Imaging Technologies Department of Computer Languages and Systems, Universitat Jaume I, Av. Vicent Sos Baynat s/n, 12071, Castellón de la Plana, Spain

    M. Millán-Giraldo, V. García & J. S. Sánchez

Authors
  1. M. Millán-Giraldo
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  2. V. García
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  3. J. S. Sánchez
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Corresponding author

Correspondence to M. Millán-Giraldo .

Editor information

Editors and Affiliations

  1. Department of Computer Languages, and Systems, Jaume I University, Vicent Sos Baynat Avenue, Castellon de la Plana, 12071, Spain

    Pedro Latorre Carmona

  2. Department of Programming Languages, and Information Systems, Jaume I University, Campus del Riu Sec s/n, Castellon de la Plana, 12071, Spain

    J. Salvador Sánchez

  3. IST - Technical University of Lisbon, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal

    Ana L.N. Fred

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Millán-Giraldo, M., García, V., Sánchez, J.S. (2013). Instance Selection Methods and Resampling Techniques for Dissimilarity Representation with Imbalanced Data Sets. In: Latorre Carmona, P., Sánchez, J., Fred, A. (eds) Pattern Recognition - Applications and Methods. Advances in Intelligent Systems and Computing, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36530-0_12

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  • DOI: https://doi.org/10.1007/978-3-642-36530-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36529-4

  • Online ISBN: 978-3-642-36530-0

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