A Parameter-Free Associative Classification Method

Authors:
Loïc Cerf;Dominique Gay;Nazha Selmaoui;Jean-François Boulicaut
Affiliations:
INSA-Lyon, LIRIS CNRS UMR5205, F-69621 Villeurbanne, France;Université de la Nouvelle-Calédonie, ERIM EA 3791, 98800 Nouméa, Nouvelle-Calédonie,;Université de la Nouvelle-Calédonie, ERIM EA 3791, 98800 Nouméa, Nouvelle-Calédonie,;INSA-Lyon, LIRIS CNRS UMR5205, F-69621 Villeurbanne, France
Venue:
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Year:
2008

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Abstract

In many application domains, classification tasks have to tackle multiclass imbalanced training sets. We have been looking for a CBA approach (Classification Based on Association rules) in such difficult contexts. Actually, most of the CBA-like methods are one-vs-all approaches (OVA), i.e., selected rules characterize a class with what is relevant for this class and irrelevant for the union of the other classes. Instead, our method considers that a rule has to be relevant for one class and irrelevant for every other class taken separately. Furthermore, a constrained hill climbing strategy spares users tuning parameters and/or spending time in tedious post-processing phases. Our approach is empirically validated on various benchmark data sets.