Rule Induction by Estimation of Distribution Algorithms

In this chapter a preliminary work on the use of Estimation of Distribution Algorithms (EDAs) for the induction of classification rules is presented. Each individual obtained by simulation of the probability distribution learnt in each EDA generation represents a disjunction of a finite number of simple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UMDA, a dependency tree and EBNAwith two classical algorithms of rule induction —RIPPER and CN2— are shown.

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