01554nas a2200157   4500008004100000245003900041210003900080260001800119300001500137520104800152100002101200700002601221700002401247700002401271856010101295       2006                        eng d00aScalability of text classification0 aScalability of text classification  c19 - 22 April  a 408 - 4133 aWe 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.
1 aBouras, Christos1 aPoulopoulos, Vassilis1 aAntonellis, Ioannis1 aZouzias, Anastasios  uhttp://telematics.upatras.gr/telematics/publications/scalability-text-classification?language=en