What Makes Categories Difficult to Classify?
Conference Proceeding Article
In this paper, we try to predict which category will be less accurately classified compared with other categories in a classification task that involves multiple categories. The categories with poor predicted performance will be identified before any classifiers are trained and additional steps can be taken to address the predicted poor accuracies of these categories. Inspired by the work on query performance prediction in ad-hoc retrieval, we propose to predict classification performance using two measures, namely, category size and category coherence. Our experiments on 20-Newsgroup and Reuters-21578 datasets show that the Spearman rank correlation coefficient between the predicted rank of classification performance and the expected classification accuracy is as high as 0.9.
classification performance prediction, text classification
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
ACM Conference on Information and Knowledge Management (CIKM)
SUN, Aixin; LIM, Ee Peng; and LIU, Ying.
What Makes Categories Difficult to Classify?. (2009). ACM Conference on Information and Knowledge Management (CIKM). 1891-1894. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/488