Improved fine-grained component-conditional class labeling with active learning

David J. Miller, Chu Fang Lin, George Kesidis, Christopher M. Collins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have recently introduced new generative semisupervised mixtures with more fine-grained class label generation mechanisms than previous methods [11], [12]. Our models combine advantages of semisupervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest- prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. Our models are advantageous when within-component class proportions are not constant over the feature space region "owned by" a component. In this paper, we develop an active learning extension of our fine-grained labeling methods. We propose two new uncertainty sampling methods in comparison with traditional entropy-based uncertainty sampling. Our experiments on a number of UC Irvine data sets show that the proposed active learning methods improve classification accuracy more than standard entropybased active learning. The proposed methods are particularly advantageous when the labeled percentage is small. We also extend our semisupervised method to allow variable weighting on labeled and unlabeled data likelihood terms. This approach is shown to outperform previous weighting schemes.

Original languageEnglish (US)
Title of host publicationProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages3-8
Number of pages6
DOIs
StatePublished - Dec 1 2010
Event9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States
Duration: Dec 12 2010Dec 14 2010

Publication series

NameProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Other

Other9th International Conference on Machine Learning and Applications, ICMLA 2010
CountryUnited States
CityWashington, DC
Period12/12/1012/14/10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Miller, D. J., Lin, C. F., Kesidis, G., & Collins, C. M. (2010). Improved fine-grained component-conditional class labeling with active learning. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 (pp. 3-8). [5708805] (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010). https://doi.org/10.1109/ICMLA.2010.8