A Set of Complexity Measures Designed for Applying Meta-Learning to
Instance Selection
Abstract:
In recent years, some authors have approached the instance selection
problem from a meta-learning perspective. In their work, they try to find
relationships between the performance of some methods from this field
and the values of some data-complexity measures, with the aim of
determining the best performing method given a data set, using only the
values of the measures computed on this data. Nevertheless, most of
the data-complexity measures existing in the literature were not conceived
for this purpose and the feasibility of their use in this field is yet to be
determined. In this paper, we revise the definition of some measures that
we presented in a previous work, that were designed for meta-learning
based instance selection. Also, we assess them in an experimental study
involving three sets of measures, 59 databases, 16 instance selection
methods, two classifiers, and eight regression learners used as metalearners. The results suggest that our measures are more efficient and
effective than those traditionally used by researchers that have addressed
the instance selection from a perspective based on meta-learning.