A multi-objective evolutionary approach for subgroup discovery

Authors:
Victoria Pachón;Jacinto Mata;Juan Luis Domínguez;Manuel J. Maña
Affiliations:
Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Spain;Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Spain;Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Spain;Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Spain
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Year:
2011

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Abstract

In this paper a new evolutionary multi-objective algorithm (GARSD) for Subgroup Discovery tasks is presented. This algorithm can work with both discrete and continuous attributes without the need for a previous discretization. An experimental study was carried out to verify the performance of the method. GAR-SD was compared with other subgroup discovery methods by evaluating certain measures (such as number of rules, number of attributes, significance, support and confidence). For Subgroup Discovery tasks, GAR-SD obtained good results compared with existing algorithms.