Building Hierarchical Classifiers Using Class Proximity
Ke Wang, Senqiang Zhou, Shiang Chen Liew · 1999
In this paper, we address the need to automatically classify text documents into topic hierarchies like those in ACM Digital Library and Yahoo!. The existing local approach constructs a classifier at each split of the topic hierarchy. However, the local approach does not address the closeness of classification in hierarchical classification where the concern often is how close a classification is, rather than simply correct or wrong. Also, the local approach puts its bet on classification at higher levels where the classification structure often diminishes. To address these issues, we propose the notion of class proximity and cast the hierarchical classification as a flat classification with the class proximity modeling the closeness of classes. Our approach is global in that it constructs a single classifier based on the global information about all classes and class proximity. We leverage generalized association rules as the rule/feature space to address several ...