Active Learning based on Hierarchical Clustering


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 10, pp. 705-712, Oct. 2013
10.3745/KTSDE.2013.2.10.705,   PDF Download:

Abstract

Active learning aims to improve the performance of a classification model by repeating the process to select the most helpful unlabeled data and include it to the training set through labelling by expert. In this paper, we propose a method for active learning based on hierarchical agglomerative clustering using Ward`s linkage. The proposed method is able to construct a training set actively so as to include at least one sample from each cluster and also to reflect the total data distribution by expanding the existing training set. While most of existing active learning methods assume that an initial training set is given, the proposed method is applicable in both cases when an initial training data is given or not given. Experimental results show the superiority of the proposed method.


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Cite this article
[IEEE Style]
H. Y. Woo and C. H. Park, "Active Learning based on Hierarchical Clustering," KIPS Transactions on Software and Data Engineering, vol. 2, no. 10, pp. 705-712, 2013. DOI: 10.3745/KTSDE.2013.2.10.705.

[ACM Style]
Ho Young Woo and Cheong Hee Park. 2013. Active Learning based on Hierarchical Clustering. KIPS Transactions on Software and Data Engineering, 2, 10, (2013), 705-712. DOI: 10.3745/KTSDE.2013.2.10.705.