Paper: Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets

ACL ID N09-2035
Title Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Venue Human Language Technologies
Session Short Paper
Year 2009
Authors

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it’s promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the fo- cused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing im- balance is to base cost models during AL on an estimate of overall corpus imbalance com- puted via a small unbiased sample rather than the imbalance in the labeled training data, which is the leading method used during PL.