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- 41 creator daniel-p-miranker.
- 41 creator mayank-kejriwal.
- 41 type InProceedings.
- 41 label "Semi-supervised Instance Matching Using Boosted Classifiers".
- 41 sameAs 41.
- 41 abstract "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. ".
- 41 hasAuthorList authorList.
- 41 isPartOf proceedings.
- 41 keyword "Boosting".
- 41 keyword "Instance Matching".
- 41 keyword "Semi-supervised Learning".
- 41 title "Semi-supervised Instance Matching Using Boosted Classifiers".