Effective instance selection using the fuzzy-rough lower approximation

Fuzzy-rough set theory has been applied with much success to the problem of feature selection, where there is a clear link between the constructs (i.e., lower approximation, positive region, etc) and the problem (i.e., finding reducts). However, there has not been much development with regards to instance selection. Previous techniques have focused on preserving the positive region or the dependency, or have concentrated on prototype selection. This paper proposes a general instance selection approach that is efficient and effective in finding reductions that maintain the integrity of the underlying class structure. By utilizing the lower approximation information, instances can be removed that have a high similarity with more representative instances of a class. In addition to a threshold-based approach, a fully automated method that requires no user input is also presented. The experimentation demonstrates that the proposed method is indeed effective at reducing the number of instances with minimal information loss.