Nonfuzzy Classification Using Rules Annotated with Weight of Evidence from Statistical Data

To design and develop a nonfuzzy classification paradigm from a statistical data set. The event association patterns of different orders are detected which provides a probabilistic inference mechanism to achieve flexible classification and prediction. To detect significant event associations, residual analysis in statistics is used. Patterns are detected and rules are generated based on the deviations of the observed patterns from a default model. The discriminative power of each rule generated is described using Weight of Evidence (WOE) statistic. Classification decisions are made using WOE based estimation of the relative likelihoods of each possible labeling. Estimates are calculated by using the set of rules triggered by matching input values. Experimental results are discussed towards the end of the paper.