A comparison of feature selection algorithms in the context of rough classifiers

We study the feature selection problem and develop and analyze four algorithms for feature selection in the context of rough set methodology. The initial state and the feasibility criterion of all these algorithms are the same, that is, they start from a given feature set and progressively remove features, while controlling the amount of degradation in classification quality, but differ in the heuristic used for pruning the search space of features. Our experimental results confirm the analytical results on the complexity of algorithms as well as on controlled degradation of upper classification. The algorithms presented can be used with any methods of deriving a classifier where the quality of classification is a monotonically decreasing function while feature set is reduced, though we have adopted the upper classifier in our study. The upper classifier has some important features that makes it suitable for database mining applications. In particular, we have shown that the upper classifier can be summarized at a desired level of abstraction by using extended decision tables. We also point out that an inconsistent decision algorithm can be interpreted as if it were a consistent decision algorithm