Robustness of adaptive filtering methods in a cross-benchmark evaluation

This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings. We found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function. Using systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Yiming Yang, Shinjae Yoo, Jian Zhang, Bryan Kisiel
• Keywords: Incremental Rocchio, Regularized Logistic Regression, SIGIR 2005, TREC Utility Function