Appropriate granularity specification for fuzzy classifier design by data complexity measures
Sinhachi NISHIKAWA, Yusuke Nojima, Hisao Ishibuchi · 2010
Tens of thousands of classifiers have been proposed so far. There is no best classifier among them. It is said that the performance of each classifier strongly depends on data sets used for comparison. In recent years, a number of data complexity measures have been proposed to characterize each data set. The aim of this study is to develop a framework for selecting an appropriate classifier and/or its appropriate parameter specification among candidate classifiers based on data complexity measures. It will be possible to clarify the domain of competence of classifiers. As a preliminary study, we propose an appropriate granularity specification method for fuzzy classifier design. First we examine a relation between the performance of classifiers with different granularities and the data complexity of artificial data sets. Next we extract if-then rule-based knowledge from the classification results on the artificial data sets.