Semi-supervised learning from unbalanced labeled data: An improvement

Issue title: Innovational Soft Computing

Article type: Research Article

Authors: Huang, Te-Ming | Kecman, Vojislav^{; *}

Affiliations: School of Engineering, The University of Auckland, 20 Symonds Street, Auckland, New Zealand

Correspondence: [*] Corresponding author. Tel.: +64 8 373 7599 (ext.88178); E-mail: [email protected]

Abstract: We present a great improvement while performing semi-supervised learning tasks from training data sets when only a small fraction of the data pairs is labeled. In particular, we propose a novel decision strategy based on normalized model outputs. We give the explanation why the normalization step helps. The paper compares performances of two popular semi-supervised approaches (Consistency Method and Harmonic Gaussian Model) on the unbalanced and balanced labeled data by using normalization of the models' outputs and without it. Experiments on text categorization problems suggest significant improvements in classification performances for models that use normalized outputs as a basis for final decision.

DOI: 10.3233/KES-2006-10102

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 10, no. 1, pp. 21-27, 2006