Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
Active learning (AL) is used in textual classification to alleviate the cost of labelling documents for training. An important issue in AL is the selection of a representative sample of documents to label for the initial training set that seeds the process, and clustering techniques have been successfully used in this regard. However, the clustering techniques used are nondeterministic which causes inconsistent behaviour in the AL process. In this paper we first illustrate the problems associated with using non-deterministic clustering for initial training set selection in AL. We then examine the performance of three deterministic clustering techniques for this task and show that performance comparable to the non-deterministic approaches can be achieved without variations in behaviour.
DOI
https://doi.org/10.21427/D7Q89W
Recommended Citation
Hu, R., Mac Namee, B. & Delany, S.J. (2010) Off to a good start: Using clustering to select the initial training set in active learning. Twenty-Third International Florida Artificial Intelligence Research Society Conference, Florida, 19 -21 May. doi:10.21427/D7Q89W
Funder
Science Foundation Ireland under Grant No. 07/RFP/CMSF718
Publication Details
Proceedings of the (FLAIRS 2010)Twenty-Third International Florida Artificial Intelligence Research Society Conference