MFILM: a multi-dimensional fuzzy inductive learning method

Abstract

Inductive learning that creates a decision tree from a set of existing examples is shown to be useful for automated knowledge acquisition. Most of the existing methods however, handle only single-dimensional decision problems. Only some methods can deal with multi-dimensional decision problems. However, they are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a multidimensional fuzzy inductive learning method that integrates the fuzzy set theory into the conventional multi-dimensional decision tree induction methods. The method converts a multi-dimensional decision tree into a fuzzy multi-dimensional decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the other conventional methods.

Using clustering for maintaining case based reasoning systems

“…The question is, which clustering method is the "best", or the most appropriate for our strategy. Actually, using inductive learning [15] is not a good choice for our case, since most inductive methods (e.g. ID3 [16] or C4.5 [17]) are expensive to run for large case bases.…”