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Approximate Entropy Reducts

Ślęzak D.

Fundamenta Informaticae

2002

Vol. 53, nr 3,4

365-390

We use information entropy measure to extend the rough set based notion of a reduct. We introduce the Approximate Entropy Reduction Principle (AERP). It states that any simplification (reduction of attributes) in the decision model, which approximately preserves its conditional entropy (the measure of inconsistency of defining decision by conditional attributes) should be performed to decrease its prior entropy (the measure of the model's complexity). We show NP-hardness of optimization tasks concerning application of various modifications of AERP to data analysis.

Ensembles of Classifiers Based on Approximate Reducts

Wróblewski J.

Fundamenta Informaticae

2001

Vol. 47, nr 3,4

351-360

The problem of improving rough set based expert systems by modifying a notion of reduct is discussed. The notion of approximate reduct is introduced, as well as some proposals of quality measure for such a reduct. The complete classifying system based on approximate reducts is presented and discussed. It is proved that the problem of finding optimal set of classifying agents based on approximate reducts is NP-hard; the genetic algorithm is applied to find the suboptimal set. Experimental results show that the classifying system is effective and relatively fast.