The DOI matches its Crossref record, with a small difference in title or year; 33 of the 38 references it cites were found in a registry or an index.
Induction of decision trees
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“For instance, it is impossible to provide a decision tree learner (Quinlan, 1986, 1993) this infinite relation because it would require an infinite feature table.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1986
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“Quinlan's (1990) FOIL system is one of the most well-known ILP systems and is a natural extension of ID3 (Quinlan, 1986) from the propositional setting to the first-order setting.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1986
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“For instance, TILDE extends a decision tree learner (Quinlan, 1986, 1993) to the first-order setting and uses the same information gain methods to induce hypotheses.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1986
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“Fault tolerance and resilience are crucial in real-time monitoring to promptly detect failures and recover to minimize data loss and downtime [7].”
From Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality (2024) · cites this paper as [7]
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Citations
Cites
Introduction to Mathematical Statistics.
Aa, Hogg, R. V., & Craig, A. T. (1996) · Journal of the American Statistical Association · doi:10.2307/2291439 · Cited by 2,907
Machine learning: An artificial intelligence approach
Hunt, E. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90037-8 · Cited by 1,375
Learning Efficient Classification Procedures and Their Application to Chess End Games
Quinlan, J. R. (1983) · Machine Learning · doi:10.1007/978-3-662-12405-5_15 · Cited by 1,104
Self-citationLearning Structural Descriptions From Examples
Winston, P. H. (1970) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 995
Introduction to Mathematical Statistics.
Doksum, K. A., Hogg, R. W., & Craig, A. T. (1971) · Journal of the American Statistical Association · doi:10.2307/2283566 · Cited by 804
(1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5 · Cited by 758
Some Studies in Machine Learning Using the Game of Checkers. II—Recent Progress
Samuel, A. L. (1967) · IBM Journal of Research and Development · doi:10.1147/rd.116.0601 · Cited by 700
Simon, H. A., Hunt, E. B., Marin, J., & Stone, P. (1967) · The American Journal of Psychology · doi:10.2307/1421207 · Cited by 659
Hunt, E. B., Marin, J., & Stone, P. J. (1966) · Medical Entomology and Zoology · Cited by 646
Learning from Observation: Conceptual Clustering
Michalski, R. S., & Stepp, R. E. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_11 · Cited by 538
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5 of the 38 references couldn’t be matched to a known paper.
Research works printed but not linked: 13
- Catlett, J. (1985). Induction using the shafer representation (Technical report). Basser Department of Computer Science, University of Sydney, Australia.
- Dechter, R., & Michie, D. (1985). Structured induction of plans and programs (Technical report). IBM Scientific Center, Los Angeles, CA.
- Feigenbaum, E.A. (1981). Expert systems in the 1980s. In A. Bond (Ed.), State of the art report on machine intelligence. Maidenhead: Pergamon-Infotech.
- Hart, A.E. (1985). Experience in the use of an inductive system in knowledge engineering. In M.A. Bramer (Ed.), Research and development in expert systems. Cambridge University Press.
- Kononenko, I., Bratko, I., & Roskar, E. (1984). Experiments in automatic learning of medical diagnostic rules (Technical report). Jozef Stefan Institute, Ljubljana, Yugoslavia.
- Michie, D. (1983). Inductive rule generation in the context of the Fifth Generation. Proceedings of the Second International Machine Learning Workshop. University of Illinois at Urbana-Champaign.
- Nilsson, N.J. (1965). Learning machines, New York: McGraw-Hill.
- Patterson, A., & Niblett, T. (1983). ACLS user manual. Glasgow: Intelligent Terminals Ltd.
- Pearl, J. (1978a). Entropy, information and rational decisions (Technical report). Cognitive Systems Laboratory, University of California, Los Angeles.
- Quinlan, J.R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.), Expert systems in the micro electronic age. Edinburgh University Press.
- Quinlan, J.R. (1982). Semi-autonomous acquisition of pattern-based knowledge. In J.E. Hayes, D. Michie & Y-H. Pao (Eds.), Machine intelligence 10. Chichester: Ellis Horwood.
- Quinlan, J.R. (1983b). Learning from noisy data, Proceedings of the Second International Machine Learning Workshop. University of Illinois at Urbana-Champaign.
- Quinlan, J.R. (1985a). The effect of noise on concept learning. In R.S. Michalski, J.G. Carbonell & T.M. Mitchell (Eds.), Machine learning. Los Altos: Morgan Kaufmann (in press).
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“Decision trees (Quinlan 1986), gradient-based explanations (Baehrens et al. 2010), and partial dependence plots (Friedman 2001) visualize decision boundaries and feature relationships, offering clinicians transparent insights into model reasoning.”
(1998) · Cited by 3,476
Metadata
- Authors
- Quinlan, J. R.
- Corresponding author
- Quinlan, J. R.
- Venue
- Machine Learning
- Published
- 1986-03
- Volume
- 1
- Issue
- 1
- Pages
- 81-106
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2149706766
- Cited by
- 21,439
- References
- 38
- Citations by year
- 2026: 899 · 2025: 1399 · 2024: 1161 · 2023: 1085 · 2022: 969 · 2021: 1165 · 2020: 1141 · 2019: 1064 · 2018: 877 · 2017: 801 · 2016: 700 · 2015: 633 · 2014: 687 · 2013: 680 · 2012: 681
- FWCI
- 255.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- decision tree induction, ID3, expert systems, noisy data, missing data, feature selection, classification
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