Hierarchical Classification of HTML Documents with WebClassII

Abstract

Abstract. This paper describes a new method for the classification of a HTML document into a hierarchy of categories. The hierarchy of categories is involved in all phases of automated document classification, namely feature extraction, learning, and classification of a new document. The innovative aspects of this work are the feature selection process, the automated threshold determination for classification scores, and an experimental study on real-word Web documents that can be associated to any node in the hierarchy. Moreover, a new measure for the evaluation of system performances has been introduced in order to compare three different techniques (flat, hierarchical with proper training sets, hierarchical with hierarchical training sets). The method has been implemented in the context of a client-server application, named WebClassII. Results show that for hierarchical techniques it is better to use hierarchical training sets.

Classifying web documents in a hierarchy of categories: a comprehensive study

“…All these results, which extend those reported in a previous work (Ceci & Malerba, 2003), are obtained by extensive experimentation on three datasets with category hierarchies of different complexity.…”

“…This paper extends and revises the work by Ceci and Malerba (2003) on hierarchical text classification. The main extensions are: 1) the consideration of hierarchical feature sets in feature selection; 2) the improvement of the naïve Bayes algorithm to avoid problems related to the different document length (Kim, Rim, Yook, & Lim, 2002); 3) the validation of the proposed framework also for a probabilistic SVMbased classifier; 4) a new automated threshold selection algorithm that operates according to a bottom-up strategy, thus taking full advantage of the decisions made at lower levels of the hierarchy; 5) a more extensive experimentation.…”

“…In a previous work (Ceci & Malerba, 2003) a top-down approach was proposed, which suffered from two limitations:…”

“…1), which include documents of a category (positive examples) and documents of the sibling categories (negative examples), are not considered for two reasons. First, in Ceci and Malerba (2003) we have already showed that hierarchical training sets perform better than proper training sets. Second, when no training document is associated to internal categories, as in the case of some datasets considered in this work, proper training sets cannot be used, since it would be impossible to build a classifier.…”

Helping Physicians to Organize Guidelines Within Conceptual Hierarchies

“…A wide range of statistical and machine learning techniques have been applied to text categorization [3][4][5][6][7][8][9]. However, these techniques are all based on having some initial labeled examples, which are used to train a (semi)-supervised model.…”