A machine learning framework for fuzzy set covering algorithms
Ian Cloete, J. van Zyl · 2005
Many machine learning algorithms for concept learning have been developed using description languages based on prepositional logic. In this paper we show how to extend the so-called set covering approach to learn classification rules based on fuzzy sets and fuzzy logic classifications. This increases the expressive power of the learning algorithm for real-valued data, and consequently extends the range of problems that can be addressed using set covering. Since instances belong to fuzzy sets to a certain degree, we design an algorithm that uses the partial ordering of fuzzy sets to construct a fuzzy lattice of concept descriptions. We illustrate the algorithm on a toy example, and present the results of real-world data sets, substantiating the claim that the increased expressive power classifies at least as well and better than comparable crisp learning algorithms.