Is Unlabeled Data Suitable for Multiclass SVM-based Web Page Classification?

Our paper “Is Unlabeled Data Suitable for Multiclass SVM-based Web Page Classification?“ has been accepted for publication and presentation at the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing to be held in Boulder, Colorado, from May 31 to June 5, 2009.

Abstract

Support Vector Machines present an interesting and effective approach to solve automated classification tasks. Although it only handles binary and supervised problems by nature, it has been transformed into multiclass and semi-supervised approaches in several works. A previous study on supervised and semi-supervised SVM classification over binary taxonomies showed how the latter clearly outperforms the former, proving the suitability of unlabeled data for the learning phase in this kind of tasks. However, the suitability of unlabeled data for multiclass tasks using SVM has never been tested before. In this work, we present a study on whether unlabeled data could improve results for multiclass web page classification tasks using Support Vector Machines. As a conclusion, we encourage to rely only on labeled data, both for improving (or at least equaling) performance and for reducing the computational cost.

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Category: research

Tags: classification, machine-learning, paper, research, semi-supervised, svm