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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Document Representations for Classification of Short Web-Page Descriptions
verfasst von : Miloš Radovanović, Mirjana Ivanović
Erschienen in: Data Warehousing and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
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Motivated by applying Text Categorization to sorting Web search results, this paper describes an extensive experimental study of the impact of bag-of-words document representations on the performance of five major classifiers – Naïve Bayes, SVM, Voted Perceptron, kNN and C4.5. The texts represent short Web-page descriptions from the
dmoz
Open Directory Web-page ontology. Different transformations of input data: stemming, normalization,
logtf
and
idf
, together with dimensionality reduction, are found to have a statistically significant improving or degrading effect on classification performance measured by classical metrics – accuracy, precision, recall, F
1
and F
2
. The emphasis of the study is not on determining the best document representation which corresponds to each classifier, but rather on describing the effects of every individual transformation on classification, together with their mutual relationships.