Rutgers Filtering Work at TREC 2002: Adaptive and Batch

Abstract

This year at TREC 2002 we participated in the adaptive filtering sub-task of the filtering track with some models for training a Rocchio classifier. Results were poorer than average on the utility type measures. Using simple feature selection produced better than average results on an F-type measure. The key to our approach was the use of pseudojudgments, and an approach to threshold updating. We also participated in the batch filtering sub-task of the filtering track and investigated the use of rank based feature selection techniques in conjunction with a very simple classification rule.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA459191

Entities

People

  • Andrei Anghelescu
  • David J. Lewis
  • David Neu
  • Endre Boros
  • Paul Kantor
  • Vladimir Menkov

Organizations

  • Rutgers University Department of Computer Science

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Adaptive Filters
  • Adaptive Training
  • Algorithms
  • Artificial Intelligence
  • Classification
  • Computer Science
  • English Language
  • Feature Selection
  • Filtration
  • Hard Copy
  • Information Retrieval
  • Judgment
  • Machine Learning
  • New Jersey
  • Training
  • Vector Spaces

Readers

  • Information Retrieval
  • Psychometric Testing or Psychological Assessment.
  • Statistical inference.