MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology

摘要

Subgroup discovery is a broadly applicable data mining technique whose main objective is the search for descriptions of subgroups of data that are statistically unusual with respect to a property of interest. The obtaining of general rules describing as many instances as possible is preferred in subgroup discovery, but this can lead to less accurate descriptions that incorrectly describe some instances. Under certain conditions, these incorrectly-described instances can be grouped into exceptions.A new post-processing methodology for the detection of exceptions associated to previously discovered subgroups is presented in this paper. The purpose is to obtain a new description to improve the accuracy of the initial subgroup and to describe new small spaces in data with unusual behaviour within the subgroup. This post-processing methodology can be applied to the results of any subgroup discovery algorithm.A post-processing multiobjective evolutionary fuzzy system is developed following this methodology, the Multiobjective Evolutionary Fuzzy system for the detection of Exceptions in Subgroups (MEFES). A wide experimental study has been performed, supported by statistical tests, comparing the results obtained by representative subgroup discovery algorithms with those obtained after applying the post-processing algorithm. Finally, MEFES is applied in a real problem related to the description of the behaviour of a type of solar cell in the Concentrating Photovoltaic area providing useful information to the experts.