In the last ten years, automatic Text Categorization (TC) has been gaining an increasing interest from the research community, due to the need to organize a massive number of digital documents. Following a machine learning paradigm, this paper presents a model which regards TC as a classification task supported by a wrapper approach and combines the utilization of a Genetic Algorithm (GA) with a filter. First, a filter is used to weigh the relevance of terms in documents. Then, the top-ranked terms are grouped in several nested sets of relatively small size. These sets are explored by a GA which extracts the subset of terms that best categorize documents. Experimental results on the Reuters-21578 dataset state the effectiveness of the proposed model and its competitiveness with the learning approaches proposed in the TC literature.
A model for term selection in text categorization problems
DESSI, NICOLETTA;DESSI', STEFANIA
2012-01-01
Abstract
In the last ten years, automatic Text Categorization (TC) has been gaining an increasing interest from the research community, due to the need to organize a massive number of digital documents. Following a machine learning paradigm, this paper presents a model which regards TC as a classification task supported by a wrapper approach and combines the utilization of a Genetic Algorithm (GA) with a filter. First, a filter is used to weigh the relevance of terms in documents. Then, the top-ranked terms are grouped in several nested sets of relatively small size. These sets are explored by a GA which extracts the subset of terms that best categorize documents. Experimental results on the Reuters-21578 dataset state the effectiveness of the proposed model and its competitiveness with the learning approaches proposed in the TC literature.
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