Sun, A.; Lim, E.-P.; Ng, W.-K.: Performance measurement framework for hierarchical text classification (2003)
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- Abstract
- Hierarchical text classification or simply hierarchical classification refers to assigning a document to one or more suitable categories from a hierarchical category space. In our literature survey, we have found that the existing hierarchical classification experiments used a variety of measures to evaluate performance. These performance measures often assume independence between categories and do not consider documents misclassified into categories that are similar or not far from the correct categories in the category tree. In this paper, we therefore propose new performance measures for hierarchicai classification. The proposed performance measures consist of category similarity measures and distance-based measures that consider the contributions of misclassified documents. Our experiments an hierarchical classification methods based an SVM classifiers and binary Naive Bayes classifiers showed that SVM classifiers perform better than Naive Bayes classifiers an Reuters-21578 collection according to the extended measures. A new classifier-centric measure called blocking measure is also defined to examine the performance of subtree classifiers in a top-down levelbased hierarchical classificatIon method.
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