Authors:
Andrea Addis
;
Giuliano Armano
and
Eloisa Vargiu
Affiliation:
University of Cagliari, Italy
Keyword(s):
Hierarchical text categorization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
The more the amount of available data (e.g., in digital libraries), the greater the need for high-performance text categorization algorithms. So far, the work on text categorization has been mostly focused on “flat” approaches, i.e., algorithms that operate on non-hierarchical classification schemes. Hierarchical approaches are expected to perform better in presence of subsumption ordering among categories. In fact, according to the “divide et impera” strategy, they partition the problem into smaller subproblems, each being expected to be simpler to solve. In this paper, we illustrate and discuss the results obtained by assessing the “Progressive Filtering” (PF) technique, used to perform text categorization. Experiments, on the Reuters Corpus (RCV1- v2) and on DZMOZ datasets, are focused on the ability of PF to deal with input imbalance. In particular, the baseline is: (i) comparing the results to those calculated resorting to the corresponding flat approach; (ii) calculating the im
provement of performance while augmenting the pipeline depth; and (iii) measuring the performance in terms of generalization- / specialization- / misclassification-error and unknown-ratio. Experimental results show that, for the adopted datasets, PF is able to counteract great imbalances between negative and positive examples.
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