Toward robust classification using the Open Directory Project
JongWoo Ha, Jung‐Hyun Lee, Won-Jun Jang, Yong-Ku Lee, SangKeun Lee · 2014
The Open Directory Project (ODP) is a large scale, high quality and publicly available web directory utilized in many studies and real-world applications. In this paper, we explore training data expansion techniques for text classification as one of the possible directions to deal with the sparse characteristic of the ODP dataset. We propose a dozen classification methods, which can be differentiated by (1) from which categories training data is expanded, and (2) how the expanded training data is merged to generate centroid vectors. Evaluation results show that training data expansion significantly improves the classification performance more than representative classifiers. We also find that (1) child and descendant categories are more valuable sources to expand training data than parent and ancestor categories, and (2) distance-based weighting is superior to simple averaging to merge the expanded training data.