Ranking Instances by Maximizing the Area under ROC Curve
This paper presents a new form of exemplar-based learning, based on a representation scheme called feature partitioning, and a particular implementation of this technique called CFP (for Classification by Feature Partitioning). Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint sets of values called segments. A segment is expanded through generalization or specialized by dividing it into sub-segments. Classification is based on a weighted voting among the individual predictions of the features, which are simply the class values of the segments corresponding to the values of a test instance for each feature. An empirical evaluation of CFP and its comparison with two other classification techniques that consider each feature separately are given.