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Classification systems are typically distinguished into single-label categorization and multi-label categorization systems, according to the number of categories they assign to each of the classified documents. In this paper, we present work undertaken in the area of single-label classification which resulted in a statistical classifier, based on the Naive Bayes assumption of statistical independence of word occurrence across a document. Our algorithm, takes into account cross-category word occurrence in deciding the class of a random document. Moreover, instead of estimating word co-occurrence in assigning a class, we estimate word contribution for a document to belong in a class. This approach outperforms other statistical classifiers as Naive Bayes Classifier and Language Models, as proven in our results.<\/jats:p>","DOI":"10.1177\/0165551511403543","type":"journal-article","created":{"date-parts":[[2011,4,18]],"date-time":"2011-04-18T23:14:49Z","timestamp":1303168489000},"page":"293-303","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["An alternative approach for statistical single-label document classification of newspaper articles"],"prefix":"10.1177","volume":"37","author":[{"given":"Georgios","family":"Mamakis","sequence":"first","affiliation":[{"name":"Technological Educational Institute of Crete, Greece and Department of Computing and Mathematical Sciences, University of Glamorgan, Wales, UK"}]},{"given":"Athanasios G.","family":"Malamos","sequence":"additional","affiliation":[{"name":"Technological Educational Institute of Crete, Greece,"}]},{"given":"J. Andrew","family":"Ware","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematical Sciences, University of Glamorgan, Wales, UK"}]}],"member":"179","published-online":{"date-parts":[[2011,4,18]]},"reference":[{"key":"atypb1","first-page":"198","volume":"3192","author":"S.B. Kotsiantis","year":"2004","journal-title":"Lecture Notes in Artificial Intelligence, AIMSA"},{"key":"atypb2","volume-title":"Proceedings of the 2nd ADBIS Workshop on Data Mining and Knowledge Discovery","author":"G. Tsoumakas"},{"key":"atypb3","volume-title":"Proceedings of the 20th International Conference on Machine Learning (ICML-2003)","author":"J.D.M. Rennie"},{"key":"atypb4","volume-title":"Proceedings of IJCAI-01 Workshop on Empirical Methods in Artificial Intelligence","author":"I. Rish"},{"key":"atypb5","volume-title":"Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"M. 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