The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A study of thresholding strategies for text categorization
Abstract
Thresholding strategies in automated text categorization are an underexplored area of research. This paper presents an examination of the effect of thresholding strategies on the performance of a classifier under various conditions. Using k-Nearest Neighbor (kNN) as the classifier and five evaluation benchmark collections as the testbets, three common thresholding methods were investigated, including rank-based thresholding (RCut), proportion-based assignments (PCut) and score-based local optimization (SCut); in addition, new variants of these methods are proposed to overcome significant problems in the existing approaches. Experimental results show that the choice of thresholding strategy can significantly influence the performance of kNN, and that the ``optimal'' strategy may vary by application. SCut is potentially better for fine-tuning but risks overfitting. PCut copes better with rare categories and exhibits a smoother trade-off in recall versus precision, but is not suitable for online decision making. RCut is most natural for online response but is too coarse-grained for global or local optimization. RTCut, a new method combining the strength of category ranking and scoring, outperforms both PCut and RCut significantly.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,827
A re-examination of text categorization methods
Yang, Y., & Liu, X. (1999) · Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/312624.312647 · Cited by 2,652
Self-citationAn Evaluation of Statistical Approaches to Text Categorization
Yang, Y. (1999) · Information Retrieval · doi:10.1023/a:1009982220290 · Cited by 1,943
Self-citationAdvances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,869
An evaluation of phrasal and clustered representations on a text categorization task
Lewis, D. D. (1992) · Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '92 · doi:10.1145/133160.133172 · Cited by 544
Training algorithms for linear text classifiers
Lewis, D. D., Schapire, R. E., Callan, J. P., & Papka, R. (1996) · Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96 · doi:10.1145/243199.243277 · Cited by 520
A Study of Approaches to Hypertext Categorization
Yang, Y., Slattery, S., & Ghani, R. (2002) · Journal of Intelligent Information Systems · doi:10.1023/a:1013685612819 · Cited by 309
Self-citationBoosting and Rocchio applied to text filtering
Schapire, R. E., Singer, Y., & Singhal, A. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.290996 · Cited by 301
Context-sensitive learning methods for text categorization
Cohen, W. W., & Singer, Y. (1996) · Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96 · doi:10.1145/243199.243278 · Cited by 252
Cited by
The Enron Corpus: A New Dataset for Email Classification Research
Klimt, B., & Yang, Y. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30115-8_22 · Cited by 1,157
Self-citationA support vector method for multivariate performance measures
Joachims, T. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102399 · Cited by 786
A Tutorial on Multilabel Learning
Gibaja, E., & Ventura, S. (2015) · ACM Computing Surveys · doi:10.1145/2716262 · Cited by 527
Personalized Search Based on User Search Histories
Speretta, M., & Gauch, S. (2005) · The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05) · doi:10.1109/wi.2005.114 · Cited by 384
Statistical topic models for multi-label document classification
Rubin, T. N., Chambers, A., Smyth, P., & Steyvers, M. (2012) · Machine Learning · doi:10.1007/s10994-011-5272-5 · Cited by 294
Metadata
- Authors
- Yang, Y.
- Venue
- Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2001-09
- Pages
- 137-145
- OpenAlex
- W1979495886
- Cited by
- 358
- References
- 12
- FWCI
- 13.18
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A study of thresholding strategies for text categorization. https://app.cochat.ai/papers/10.1145/383952.383975
- The paper
- Yang, Y. (2001). A study of thresholding strategies for text categorization. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/383952.383975
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
