Semi-supervised Instance Matching Using Boosted Classifiers

author: Mayank Kejriwal, Department of Computer Science, University of Texas at Austin
published: July 15, 2015,   recorded: June 2015,   views: 1555


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Instance matching concerns identifying pairs of instances that refer to the same underlying entity. Current state-of-the-art instance matchers use machine learning methods. Supervised learning systems achieve good performance by training on significant amounts of manually labeled samples. To alleviate the labeling effort, this paper presents a minimally supervised instance matching approach that is able to deliver competitive performance using only 2% training data and little parameter tuning. As a first step, the classifier is trained in an ensemble setting using boosting. Iterative semi-supervised learning is used to improve the performance of the boosted classifier even further, by re-training it on the most confident samples labeled in the current iteration. Empirical evaluations on a suite of six publicly available benchmarks show that the proposed system outcompetes optimization-based minimally supervised approaches in 1–7 iterations. The system’s average F-Measure is shown to be within 2.5% of that of recent supervised systems that require more training samples for effective performance.

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Download slides icon Download slides: eswc2015_kejriwal_boosted_classifiers_01.pdf (952.5 KB)

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