FOIL: A midterm report
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CPAR combines the positive aspects of both associative classification and traditional rule-based classification, does not generate a large set of candidates as in associative classification, and applies a greedy approach for generating rules c_g directly from training data.
“In [109] the authors propose the approach f named CPAR (Classification based on Predictive Association Rules) combining the positive aspects of both associative classification and traditional rule-based classification. Indeed, following the basic idea of FOIL [80], CPAR does not generate a large set of candidates as in associative classification, and applies a greedy approach for generating rules”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [80]
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Cites
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417 · see citing works
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644 · see citing works
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
“Indeed, following the basic idea of FOIL [94], CPAR does not generate a large set of candidates, as in associative classification, and applies a greedy approach for generating rules cд directly from training data.”
Meta-Interpretive Learning of Data Transformation Programs
Cropper, A., Tamaddoni-Nezhad, A., & Muggleton, S. H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-40566-7_4 · Cited by 32 · see citing works
- Authors
- Quinlan, J. R., & Cameron-Jones, R. M.
- Affiliations
- The University of Sydney (AU)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1993
- Pages
- 1-20
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1531743498
- Cited by
- 528
- References
- 2
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 11 · 2022: 8 · 2021: 5 · 2020: 12 · 2019: 11 · 2018: 17 · 2017: 11 · 2016: 15 · 2015: 16 · 2014: 23 · 2013: 30 · 2012: 31
- FWCI
- 14.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Australian Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- foil, inductive logic programming, program synthesis from examples, Prolog, learning system evaluation
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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.
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