The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Chi2: feature selection and discretization of numeric attributes
Abstract
Discretization can turn numeric attributes into discrete ones. Feature selection can eliminate some irrelevant attributes. This paper describes Chi2 a simple and general algorithm that uses the /spl chi//sup 2/ statistic to discretize numeric attributes repeatedly until some inconsistencies are found in the data, and achieves feature selection via discretization. The empirical results demonstrate that Chi/sup 2/ is effective in feature selection and discretization of numeric and ordinal attributes.
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
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
The feature selection problem: traditional methods and a new algorithm
Kira, K., & Rendell, L. A. (1992) · National Conference on Artificial Intelligence · Cited by 1,897
ChiMerge: discretization of numeric attributes
Kerber, R. (1992) · National Conference on Artificial Intelligence · Cited by 657
Learning Boolean concepts in the presence of many irrelevant features
Almuallim, H., & Dietterich, T. G. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90084-1 · Cited by 520
On changing continuous attributes into ordered discrete attributes
Catlett, J. (1991) · Lecture Notes in Computer Science · doi:10.1007/bfb0017012 · Cited by 426
The attribute selection problem in decision tree generation
Fayyad, U. M., & Irani, K. B. (1992) · National Conference on Artificial Intelligence · Cited by 218
Lookahead Feature Construction for Learning Hard Concepts
Ragavan, H., & Rendell, L. A. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50039-3 · Cited by 79
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
(1998) · Cited by 3,476
Discretization: An Enabling Technique
Liu, H., Hussain, F., Tan, C. L., & Dash, M. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1016304305535 · Cited by 981
Self-citationA review of feature selection methods on synthetic data
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830
Distributional clustering of words for text classification
Baker, L. D., & McCallum, A. K. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.290970 · Cited by 676
Metadata
- Authors
- Liu, H., & Setiono, R.
- Venue
- Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 388-391
- OpenAlex
- W2109293916
- Cited by
- 933
- References
- 8
- FWCI
- 14.80
- 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: Chi2: feature selection and discretization of numeric attributes. https://app.cochat.ai/papers/10.1109/tai.1995.479783
- The paper
- Liu, H., & Setiono, R. (2002). Chi2: feature selection and discretization of numeric attributes. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence. https://doi.org/10.1109/tai.1995.479783
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.
