Data-based research at IIT Bombay
ACM SIGMOD Record
How the live web feels about events
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The goal of this work is to estimate the accuracy of a classifier on a large unlabeled dataset based on a small labeled set and a human labeler. We seek to estimate accuracy and select instances for labeling in a loop via a continuously refined stratified sampling strategy. For stratifying data we develop a novel strategy of learning r bit hash functions to preserve similarity in accuracy values. We show that our algorithm provides better accuracy estimates than existing methods for learning distance preserving hash functions. Experiments on a wide spectrum of real datasets show that our estimates achieve between 15% and 62% relative reduction in error compared to existing approaches. We show how to perform stratified sampling on unlabeled data that is so large that in an interactive setting even a single sequential scan is impractical. We present an optimal algorithm for performing importance sampling on a static index over the data that achieves close to exact estimates while reading three orders of magnitude less data.