Ioannis Antonellis, Christos Bouras, Vassilis Poulopoulos, Anastasios Zouzias



We explore scalability issues of the text classification problem where using (multi)labeled training documents we try to build classifiers that assign documents into classes permitting classification in multiple classes. A new class of classification problems, called ‘scalable’ is introduced that models many problems from the area of Web mining. The property of scalability is defined as the ability of a classifier to adjust classification results on a ‘per-user’ basis. Furthermore, we investigate on different ways to interpret personalization of classification results by analyzing well known text datasets and exploring existent classifiers. We present solutions for the scalable classification problem based on standard classification techniques and present an algorithm that relies on the semantic analysis using document decomposition into its sentences. Experimental results concerning the scalability property and the performance of these algorithms are provided using the 20newsgroup dataset and a dataset consisting of web news.


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Paper Citation

in Harvard Style

Antonellis I., Bouras C., Poulopoulos V. and Zouzias A. (2006). SCALABILITY OF TEXT CLASSIFICATION . In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-972-8865-46-7, pages 408-413. DOI: 10.5220/0001240904080413

in Bibtex Style

author={Ioannis Antonellis and Christos Bouras and Vassilis Poulopoulos and Anastasios Zouzias},
booktitle={Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
SN - 978-972-8865-46-7
AU - Antonellis I.
AU - Bouras C.
AU - Poulopoulos V.
AU - Zouzias A.
PY - 2006
SP - 408
EP - 413
DO - 10.5220/0001240904080413