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

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.

[1]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[2]  Ronald W. Schafer,et al.  Digital Processing of Speech Signals , 1978 .

[3]  E. Ziegel,et al.  Proceedings in Computational Statistics , 1998 .

[4]  Florin Cutzu,et al.  Polychotomous Classification with Pairwise Classifiers: A New Voting Principle , 2003, Multiple Classifier Systems.

[5]  Alexander J. Smola,et al.  Adaptive Margin Support Vector Machines , 2000 .

[6]  Claudio Conversano,et al.  Simultaneous Threshold Interaction Detection in Binary Classification , 2010 .

[7]  Gerald W. Kimble,et al.  Information and Computer Science , 1975 .

[8]  Antony Unwin,et al.  Graphics of a Large Dataset , 2006 .

[9]  Claudio Conversano,et al.  Sequential Automatic Search of a Subset of Classifiers in Multiclass Learning , 2008 .

[10]  Alfred Inselberg Visual Data Mining with Parallel Coordinates , 1998 .

[11]  Younès Bennani,et al.  Dendogram based SVM for multi-class classification , 2006, 28th International Conference on Information Technology Interfaces, 2006..

[12]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[13]  David G. Stork,et al.  Pattern Classification , 1973 .

[14]  Matthew Brand,et al.  Pattern discovery via entropy minimization , 1999, AISTATS.

[15]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[16]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[17]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[18]  David J. Slate,et al.  Letter Recognition Using Holland-Style Adaptive Classifiers , 1991, Machine Learning.

[19]  Christopher M. Bishop,et al.  Classification and regression , 1997 .

[20]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[21]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[22]  Terence C. Fogarty,et al.  Technical Note: First Nearest Neighbor Classification on Frey and Slate's Letter Recognition Problem , 1992, Machine Learning.

[23]  E. Paradis Analysis of Phylogenetics and Evolution with R , 2006 .

[24]  Claudio Conversano,et al.  Detecting subset of classifiers for Multi-Attribute response prediction , 2010 .

[25]  H. Hofmann Mosaic Plots and Their Variants , 2008 .

[26]  Simon Urbanek Different Ways to See a Tree - KLIMT , 2002, COMPSTAT.

[27]  Lars Linsen,et al.  Data Viz VI , 2011, Comput. Stat..

[28]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[29]  Maurizio Vichi,et al.  Studies in Classification Data Analysis and knowledge Organization , 2011 .