Enhanced hierarchical classification via isotonic smoothing

Hierarchical topic taxonomies have proliferated on the World Wide Web [5, 18], and exploiting the output space decompositions they induce in automated classification systems is an active area of research. In many domains, classifiers learned on a hierarchy of classes have been shown to outperform those learned on a flat set of classes. In this paper we argue that the hierarchical arrangement of classes leads to intuitive relationships between the corresponding classifiers' output scores, and that enforcing these relationships as a post-processing step after classification can improve its accuracy. We formulate the task of smoothing classifier outputs as a regularized isotonic tree regression problem, and present a dynamic programming based method that solves it optimally. This new problem generalizes the classic isotonic tree regression problem, and both, the new formulation and algorithm, might be of independent interest. In our empirical analysis of two real-world text classifi...

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Kunal Punera, Joydeep Ghosh
• Keywords: Internet Technology, Isotonic Tree Regression, Smoothing Classifier Outputs, Tree Regression Problem, WWW 2008