Authors:
Thanh-Binh Le
and
Sang-Woon Kim
Affiliation:
Myongji University, Korea, Republic of
Keyword(s):
Semi-supervised MarginBoost, Incremental learning strategy, Dissimilarity-based classifications.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Pattern Recognition
;
Reinforcement Learning
;
Theory and Methods
Abstract:
The aim of this paper is to present an incremental learning strategy by which the classification accuracy of
the semi-supervised MarginBoost (SSMB) algorithm (d’Alch ´ e Buc, 2002) can be improved. In SSMB, both a limited number of labeled and a multitude of unlabeled data are utilized to learn a classification model. However, it is also well known that the utilization of the unlabeled data is not always helpful for semi-supervised learning algorithms. To address this concern when dealing with SSMB, in this paper we study a means of selecting only “small” helpful portion of samples from the additional available data. More specifically, this is done by performing SSMB after incrementally reinforcing the given labeled training data with a part of strong unlabeled data; we train the classification model in an incremental fashion by employing a small amount of “strong” samples selected from the unlabeled data per iteration. The proposed scheme is evaluated with well-known benchmark datab
ases, including some UCI data sets, in two approaches: dissimilarity-based classification (DBC) (Pekalska and Duin, 2005) as well as conventional feature-based classification. Our experimental results demonstrate that, compared to previous approaches, it achieves better classification accuracy results.
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