Methods for Constructing Symbolic Ensembles from Symbolic Classifiers

摘要

Practical Data Mining applications use learning algorithms to induce knowledge. Thus, these algorithms should be able to operate in massive datasets. Techniques such as dataset sampling can be used to scale up learning algorithms to large datasets. A general approach associated with sampling is the construction of ensembles of classifiers, which can be more accurate than the individual classifiers. However, ensembles often lack the facility to explain its decisions. In this work we explore a method for constructing ensembles of symbolic classifiers, such that the ensembles are able to explain its decisions to the user. This idea has been implemented in the ELE system described in this work.