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Interactive visualization in multiclass learning: integrating the SASSC algorithm with KLIMT

Computational Statistics volume 26, Article number: 711 (2011) Cite this article

Abstract

The paper deals with multiclass learning from the perspective of analytically interpreting the results of the analysis as well as that of navigating into them by using interactive visualization tools. It is showed that by combining the Sequential Automatic Search of Subset of Classifiers (SASSC) algorithm with the interactive visualization of classification trees provided by the Klassification—Interactive Methods for Trees (KLIMT) software it is possible to highlight important information deriving from the knowledge extraction process without neglecting the prediction accuracy of the classification method. Empirical evidence from two benchmark datasets demonstrates the advantages deriving from the joint use of SASSC and KLIMT.

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  1. Department of Economics, University of Cagliari, Cagliari, Italy

    Claudio Conversano

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  1. Claudio Conversano
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Correspondence to Claudio Conversano.

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Conversano, C. Interactive visualization in multiclass learning: integrating the SASSC algorithm with KLIMT. Comput Stat 26, 711 (2011). https://doi.org/10.1007/s00180-011-0255-3

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  • DOI: https://doi.org/10.1007/s00180-011-0255-3

Keywords

  • Classification
  • Multiclass response
  • Subset selection
  • Semi-supervised learning
  • CART
  • SASSC
  • Phylogenetic tree
  • KLIMT
  • Interactive visualization