Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection

Abstract

This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications. Algorithms based on closest-to-hyperplane selection and query-by-committee selection are combined with methods for addressing imbalance such as positive amplification based on prevalence statistics from initial random samples. Three algorithms (ClosestPA, QBagPA, and QBoostPA) are presented and carefully evaluated on datasets for text classification and relation extraction. The ClosestPA algorithm is shown to consistently outperform the other two in a variety of ways and insights are provided as to why this is the case.

arXiv comment8 pages, 7 figures, 3 tables; published in Proceedings of the IEEE 12th International Conference on Semantic Computing (ICSC 2018), Laguna Hills, CA, USA, pages 148-155, January 2018

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Cited by in corpus (7)

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• Stopping Active Learning based on Predicted Change of F Measure for Text Classification
• The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
• Impact of Batch Size on Stopping Active Learning for Text Classification
• Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning
• A Simple yet Brisk and Efficient Active Learning Platform for Text Classification
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