|Titolo:||A model for term selection in text categorization problems|
|Data di pubblicazione:||2012|
|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.|
|Tipologia:||4.1 Contributo in Atti di convegno|
File in questo prodotto:
Non ci sono file associati a questo prodotto.