Induction of decision trees
Open accessThe 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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
It is impossible to provide a decision tree learner this infinite relation because it would require an infinite feature table.
“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
Quinlan's (1990) FOIL system is one of the most well-known ILP systems and is a natural extension of ID3 from the propositional setting to the first-order setting.
“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
TILDE extends a decision tree learner to the first-order setting and uses the same information gain methods to induce hypotheses.
“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
Fault tolerance and resilience are crucial in real-time monitoring to promptly detect failures and recover to minimize data loss and downtime.
“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]
The Noise section (pp. 12–16) and Unknown attribute values section (p. 19) discuss how decision trees handle corrupted or missing data. Searches for real-time monitoring, failure detection, recovery, data loss, and downtime found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 2,914 · see citing works
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 · see citing works
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 · see citing works
Self-citationLearning Structural Descriptions From Examples
Winston, P. H. (1970) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 995 · see citing works
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 · see citing works
(1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5 · Cited by 758 · see citing works
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 · see citing works
Simon, H. A., Hunt, E. B., Marin, J., & Stone, P. (1967) · The American Journal of Psychology · doi:10.2307/1421207 · Cited by 659 · see citing works
Hunt, E. B., Marin, J., & Stone, P. J. (1966) · Medical Entomology and Zoology · Cited by 646 · see citing works
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 · see citing works
An Overview of Machine Learning
Carbonell, J., Michalski, R. S., & Mitchell, T. M. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_1 · Cited by 474 · see citing works
Pattern Recognition as Rule-Guided Inductive Inference
Michalski, R. S. (1980) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1980.4767034 · Cited by 405 · see citing works
Concept learning: An information processing problem.
Hunt, E. B. (1962) · John Wiley & Sons, Inc. eBooks · doi:10.1037/13135-000 · Cited by 373 · see citing works
Introduction to Mathematical Statistics.
HITCHCOCK, S. E., Hogg, R. V., & Craig, A. T. (1966) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2982264 · Cited by 316 · see citing works
Proceedings of the Eighth International Joint Conference on Artificial Intelligence
Bundy, A. (1983) · Cited by 296 · see citing works
Introduction to Mathematical Statistics
Hogg, R. V. C. 2. J., McKean, J. W., & Craig, A. T. (1965) · Cited by 290 · see citing works
AN OVERVIEW OF MACHINE LEARNING
Carbonell, J., Michalski, R. S., & Mitchell, T. M. (1983) · Elsevier eBooks · doi:10.1016/b978-0-08-051054-5.50005-4 · Cited by 260 · see citing works
An inference technique for integrating knowledge from disparate sources
Garvey, T. D., Lowrance, J. D., & Fischler, M. A. (1981) · Cited by 220 · see citing works
REDISCOVERING CHEMISTRY WITH THE BACON SYSTEM
Pat Langley, B., Bradshaw, G. L., & Simon, H. A. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_10 · Cited by 178 · see citing works
Buchanan, B. G., & Mitchell, T. M. (1978) · Elsevier eBooks · doi:10.1016/b978-0-12-737550-2.50019-1 · Cited by 151 · see citing works
Decision trees and multi-valued attributes
Quilan, J. R. (1988) · Machine intelligence · Cited by 122 · see citing works
LEARNING FROM OBSERVATION: CONCEPTUAL CLUSTERING
Michalski, R. S., & Stepp, R. E. (1983) · Elsevier eBooks · doi:10.1016/b978-0-08-051054-5.50015-7 · Cited by 112 · see citing works
Model-directed learning of production rules
Buchanan, B. G., & Mitchell, T. M. (1977) · ACM SIGART Bulletin · doi:10.1145/1045343.1045371 · Cited by 110 · see citing works
ON THE CONNECTION BETWEEN THE COMPLEXITY AND CREDIBILITY OF INFERRED MODELS
Pearl, J. (1978) · International Journal of General Systems · doi:10.1080/03081077808960690 · Cited by 98 · see citing works
An appraisal of a decision tree approach to image classification
Shepherd, B. (1983) · Cited by 53 · see citing works
Model-Directed Learning of Production Rules
Buchanan, B. G., & Mitchell, T. M. (1977) · doi:10.21236/ada042721 · Cited by 24 · see citing works
Performance of a reading task by an elementary perceiving and memorizing program
Feigenbaum, E. A., & Simon, H. A. (2007) · Systems Research and Behavioral Science · doi:10.1002/bs.3830080109 · Cited by 22 · see citing works
Role of structured induction in expert systems
Shapiro, A. D. (1984) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 15 · see citing works
Concept development for Expert System Knowledge Bases.
Sammut, C. (1985) · Australian Computer Journal · Cited by 13 · see citing works
Experiments on the Mechanization of Game-learning. 2--Rule-Based Learning and the Human Window
Michie, D. (1982) · The Computer Journal · doi:10.1093/comjnl/25.1.105 · Cited by 12 · see citing works
A task-independent experience-gathering scheme for a problem-solver
Quinlan, J. R. (1969) · International Joint Conference on Artificial Intelligence · Cited by 11 · see citing works
Self-citationCURRENT DEVELOPMENTS IN ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS
Michie, D. (1985) · Zygon® · doi:10.1111/j.1467-9744.1985.tb00604.x · Cited by 10 · see citing works
Concept formation from very large training sets
O’Keefe, R. A. (1983) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 8 · see citing works
Flagged first, then by citations
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).
Cited by
Sorted by citations
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,655 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907 · see citing works
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097 · see citing works
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,940 · see citing works
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951 · see citing works
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636 · see citing works
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583 · see citing works
“One-R [47], decision trees [87, 88], DTNB [110], Ripple Down Rule learner (RIDOR) [125], Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [126] exist with the ability of rule generation.”
Instance-Based Learning Algorithms
Aha, D. W., Kibler, D. F., & Albert, M. K. (1991) · Machine Learning · doi:10.1023/a:1022689900470 · Cited by 5,217 · see citing works
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555 · see citing works
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551 · see citing works
A Bayesian Method for the Induction of Probabilistic Networks from Data
Cooper, G. F., & Herskovits, E. H. (1992) · Machine Learning · doi:10.1023/a:1022649401552 · Cited by 4,194 · see citing works
Bayesian Networks and Decision Graphs
Jensen, F. V., & Nielsen, T. D. (2007) · Information science and statistics · doi:10.1007/978-0-387-68282-2 · Cited by 4,157 · see citing works
A survey of decision tree classifier methodology
Safavian, S. R., & Landgrebe, D. A. (1991) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.97458 · Cited by 3,805 · see citing works
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596 · see citing works
Feature Selection for Classification
Dash, M., & Liu, H. (1997) · Intelligent Data Analysis · doi:10.1016/s1088-467x(97)00008-5 · Cited by 3,594 · see citing works
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A. W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995316 · Cited by 3,545 · see citing works
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332 · see citing works
Sentiment analysis algorithms and applications: A survey
Medhat, W., Hassan, A., & Korashy, H. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2014.04.011 · Cited by 3,265 · see citing works
Estimating attributes: Analysis and extensions of RELIEF
Kononenko, I. (1994) · Lecture notes in computer science · doi:10.1007/3-540-57868-4_57 · Cited by 3,062 · see citing works
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · see citing works
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870 · see citing works
(2015) · Cited by 2,857 · see citing works
Hierarchical Mixtures of Experts and the EM Algorithm
Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,730 · see citing works
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,655 · see citing works
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600 · see citing works
Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583 · see citing works
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553 · see citing works
Self-citationModern Applied Statistics with S Fourth edition
Venables, W. N., & Ripley, B. D. (2002) · Cited by 2,538 · see citing works
Irrelevant Features and the Subset Selection Problem
John, G. H., Kohavi, R., & Pfleger, K. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50023-4 · Cited by 2,403 · see citing works
Robust real-time object detection.
Viola, P. A. (2001) · Cited by 2,340 · see citing works
Classifier chains for multi-label classification
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011) · Machine Learning · doi:10.1007/s10994-011-5256-5 · Cited by 2,312 · see citing works
Data mining: an overview from a database perspective
Chen⋆, M.-S., Han, J., & Yu, P. S. (1996) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.553155 · Cited by 2,240 · see citing works
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239 · see citing works
Implementation of machine-learning classification in remote sensing: an applied review
Maxwell, A. E., Warner, T. A., & Fang, F. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1433343 · Cited by 2,152 · see citing works
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,050 · see citing works
Survey of intrusion detection systems: Techniques, datasets and challenges
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019) · Cybersecurity · doi:10.1186/s42400-019-0038-7 · Cited by 2,024 · see citing works
Independent Component Analysis
(2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511624148 · Cited by 1,949 · see citing works
An Evaluation of Statistical Approaches to Text Categorization
Yang, Y. (1999) · Information Retrieval · doi:10.1023/a:1009982220290 · Cited by 1,945 · see citing works
Faster and Better: A Machine Learning Approach to Corner Detection
Rosten, E., Porter, R. B., & Drummond, T. W. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.275 · Cited by 1,852 · see citing works
Roberto Navigli, A. (2009) · ACM Computing Surveys · doi:10.1145/1459352.1459355 · Cited by 1,843 · see citing works
Group formation in large social networks: membership, growth, and evolution
Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150412 · Cited by 1,805 · see citing works
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
Holte, R. C. (1993) · Machine Learning · doi:10.1023/a:1022631118932 · Cited by 1,800 · see citing works
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777 · see citing works
Graph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
Sun, Y., Wong, A. K. C., & Kamel, M. S. (2009) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001409007326 · Cited by 1,716 · see citing works
A survey of content-based image retrieval with high-level semantics
Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.045 · Cited by 1,696 · see citing works
Machine learning for medical imaging
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672 · see citing works
EXPERT SYSTEMS WITH APPLICATIONS
Communication, S., Been-chian Chien, A., Jung Yi Lin, A., Tzung-pei Hong, B., & Com/locate/eswa, W. E. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.10.010 · Cited by 1,670 · see citing works
Machine learning for medical diagnosis: history, state of the art and perspective
Kononenko, I. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(01)00077-x · Cited by 1,674 · see citing works
Text Classification Algorithms: A Survey
Kowsari, K., Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L. E., & Brown, D. E. (2019) · Inf. · doi:10.3390/info10040150 · Cited by 1,605 · 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
Self-citationComparing different supervised machine learning algorithms for disease prediction
Uddin, S., Khan, A. M., Hossain, M. E., & Moni, M. A. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1004-8 · Cited by 1,687 · see citing works
A Survey on Deep Learning for Named Entity Recognition
Li, J., Sun, A., Han, J., & Li, C. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981314 · Cited by 1,667 · see citing works
Introduction to Machine Learning
Yu, F. R., & He, Y. (2019) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-030-10546-4_1 · Cited by 1,619 · see citing works
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.06.004 · Cited by 1,611 · see citing works
Decision tree classification of land cover from remotely sensed data
Friedl, M. A., & Brodley, C. E. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00049-7 · Cited by 1,543 · see citing works
Inductive Logic Programming: Theory and methods
Muggleton, S., & de Raedt, L. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90035-3 · Cited by 1,537 · see citing works
Brill, E. (1995) · Cited by 1,530 · see citing works
Database mining: a performance perspective
Agrawal, R., Imieliński, T., & Swami, A. (1993) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.250074 · Cited by 1,514 · see citing works
Cost-sensitive boosting for classification of imbalanced data
Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.009 · Cited by 1,429 · see citing works
An effective hash-based algorithm for mining association rules
Park, J. S., Chen⋆, M.-S., & Yu, P. S. (1995) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/223784.223813 · Cited by 1,420 · see citing works
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392 · see citing works
A review of clustering techniques and developments
Saxena, A. K., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W., & Lin, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.06.053 · Cited by 1,367 · see citing works
Network Anomaly Detection: Methods, Systems and Tools
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.052213.00046 · Cited by 1,358 · see citing works
A Framework for Collaborative, Content-Based and Demographic Filtering
Pazzani, M. J. (1999) · Artificial Intelligence Review · doi:10.1023/a:1006544522159 · Cited by 1,334 · see citing works
Normalized Mutual Information Feature Selection
Estévez, P. A., Tesmer, M., Perez, C. A., & Żurada, J. M. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005601 · Cited by 1,293 · see citing works
Shape Quantization and Recognition with Randomized Trees
Amit, Y., & Geman, D. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1545 · Cited by 1,277 · see citing works
Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A. (2019) · 2019 International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/ccst.2019.8888419 · Cited by 1,275 · see citing works
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., Pham, B. T., Bui, D. T., Avtar, R., & Boumezbeur, A. (2020) · Earth-Science Reviews · doi:10.1016/j.earscirev.2020.103225 · Cited by 1,292 · see citing works
Local Invariant Feature Detectors: A Survey
Tuytelaars, T., & Mikolajczyk, K. (2007) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000017 · Cited by 1,218 · see citing works
Predicting breast cancer survivability: a comparison of three data mining methods
Delen, D., Walker, G., & Kadam, A. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.07.002 · Cited by 1,215 · see citing works
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208 · see citing works
Learning and Revising User Profiles: The Identification of Interesting Web Sites
Pazzani, M. J., & Billsus, D. (1997) · Machine Learning · doi:10.1023/a:1007369909943 · Cited by 1,208 · see citing works
Pradhan, B. (2012) · Computers & Geosciences · doi:10.1016/j.cageo.2012.08.023 · Cited by 1,207 · see citing works
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
Barua, S., Islam, M. M., Yao, X., & Murase, K. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.232 · Cited by 1,165 · see citing works
Data-Driven Science and Engineering
Brunton, S. L., & Kutz, J. N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108380690 · Cited by 1,177 · see citing works
Interestingness measures for data mining
Geng, L., & Hamilton, H. J. (2006) · ACM Computing Surveys · doi:10.1145/1132960.1132963 · Cited by 1,151 · see citing works
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.040 · Cited by 1,141 · see citing works
A Survey of Dimension Reduction Techniques
Fodor, I. K. (2002) · doi:10.2172/15002155 · Cited by 1,109 · see citing works
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122 · see citing works
Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
Tam, K. Y., & Kiang, M. Y. (1992) · Management Science · doi:10.1287/mnsc.38.7.926 · Cited by 1,122 · see citing works
Applications of Machine Learning in Cancer Prediction and Prognosis
Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,113 · see citing works
Big Data Analytics in Intelligent Transportation Systems: A Survey
Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2815678 · Cited by 1,079 · see citing works
Privacy Preserving Data Mining
Lindell, Y., & Pinkas, B. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44598-6_3 · Cited by 1,077 · see citing works
Bayesian Artificial Intelligence
Korb, K. B., & Nicholson, A. E. (2003) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203491294 · Cited by 1,074 · see citing works
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069 · see citing works
The adaptive nature of human categorization.
Anderson, J. R. (1991) · Psychological Review · doi:10.1037/0033-295x.98.3.409 · Cited by 1,069 · see citing works
Accurate predictions on small data with a tabular foundation model
Hollmann, N., Müller, S., Purucker, L., Krishnakumar, A., Körfer, M., Hoo, S. B., Schirrmeister, R. T., & Hutter, F. (2025) · Nature · doi:10.1038/s41586-024-08328-6 · Cited by 1,119 · see citing works
Hand, D. J. (2007) · Drug Safety · doi:10.2165/00002018-200730070-00010 · Cited by 1,057 · see citing works
Learning Collaborative Information Filters
Billsus, D., & Pazzani, M. J. (1998) · Cited by 1,040 · see citing works
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035 · see citing works
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works
A Hybrid Approach to Privacy-Preserving Federated Learning
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., & Zhou, Y. (2019) · Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3338501.3357370 · Cited by 1,029 · see citing works
Feature selection for classification: A review
Tang, J., Alelyani, S., & Liu, H. (2014) · doi:10.1201/b17320-7 · Cited by 996 · see citing works
A Survey on the Explainability of Supervised Machine Learning
Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008 · see citing works
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J.-Y., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 977 · see citing works
- 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
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
