Rule-based machine learning

Short description: AI that learns decision rules from data

Machine learning and
data mining

Problems

* Classification
* Clustering
* Regression
* Anomaly detection
* AutoML
* Association rules
* Reinforcement learning
* Structured prediction
* Feature engineering
* Feature learning
* Online learning
* Semi-supervised learning
* Unsupervised learning
* Learning to rank
* Grammar induction

Supervised learning
(classification • regression)

* Decision trees
* Ensembles
* Bagging
* Boosting
* Random forest
* k-NN
* Linear regression
* Naive Bayes
* Artificial neural networks
* Logistic regression
* Perceptron
* Relevance vector machine (RVM)
* Support vector machine (SVM)

Clustering

* BIRCH
* CURE
* Hierarchical
* k-means
* Expectation–maximization (EM)

DBSCAN
* OPTICS
* Mean-shift

Dimensionality reduction

* Factor analysis
* CCA
* ICA
* LDA
* NMF
* PCA
* t-SNE

Structured prediction

* Graphical models
* Bayes net
* Conditional random field
* Hidden Markov

Anomaly detection

* k-NN
* Local outlier factor

Artificial neural networks

* Autoencoder
* Deep learning
* DeepDream
* Multilayer perceptron
* RNN
* LSTM
* GRU
* Restricted Boltzmann machine
* GAN
* SOM
* Convolutional neural network
* U-Net

Reinforcement learning

* Q-learning
* SARSA
* Temporal difference (TD)

Theory

* Bias–variance dilemma
* Computational learning theory
* Empirical risk minimization
* Occam learning
* PAC learning
* Statistical learning
* VC theory

Machine-learning venues

* NeurIPS
* ICML
* ML
* JMLR
* ArXiv:cs.LG

Glossary of artificial intelligence

* Glossary of artificial intelligence

Related articles

* List of datasets for machine-learning research
* Outline of machine learning

* v
* t
* e

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply.[1][2][3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.

Rule-based machine learning approaches include learning classifier systems,[4] association rule learning,[5] artificial immune systems,[6] and any other method that relies on a set of rules, each covering contextual knowledge.

While rule-based machine learning is conceptually a type of rule-based system, it is distinct from traditional rule-based systems, which are often hand-crafted, and other rule-based decision makers. This is because rule-based machine learning applies some form of learning algorithm such as Rough sets theory[7] to identify and minimise the set of features and to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and curate a rule set.

## Contents

* 1 Rules
* 2 RIPPER
* 3 See also
* 4 References

## Rules

Rules typically take the form of an '{IF:THEN} expression', (e.g. {IF 'condition' THEN 'result'}, or as a more specific example, {IF 'red' AND 'octagon' THEN 'stop-sign}). An individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Therefore rule-based machine learning methods typically comprise a set of rules, or knowledge base, that collectively make up the prediction model usually known as decision algorithm. Rules can also be interpreted in various ways depending on the domain knowledge, data types(discrete or continuous) and in combinations.

## RIPPER

Repeated incremental pruning to produce error reduction (RIPPER) is a propositional rule learner proposed by William W. Cohen as an optimized version of IREP.[8]

## See also

* Learning classifier system
* Association rule learning
* Associative classifier
* Artificial immune system
* Expert system
* Decision rule
* Rule induction
* Inductive logic programming
* Rule-based machine translation
* Genetic algorithm
* Rule-based system
* Rule-based programming
* RuleML
* Production rule system
* Business rule engine
* Business rule management system

## References

1. ↑ Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Bacardit, Jaume (2011-09-01). "Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets" (in en). The Plant Cell 23 (9): 3101–3116. doi:10.1105/tpc.111.088153. ISSN 1532-298X. PMID 21896882. Bibcode: 2011PlanC..23.3101B.
2. ↑ M., Weiss, S.; N., Indurkhya (1995-01-01). "Rule-based Machine Learning Methods for Functional Prediction". Journal of Artificial Intelligence Research 3 (1995): 383–403. doi:10.1613/jair.199. Bibcode: 1995cs.......12107W. http://jair.org/papers/paper199.html.
3. ↑ "GECCO 2016 | Tutorials". http://gecco-2016.sigevo.org/index.html/Tutorials#id_Introducing rule-based machine learning: capturing complexity.
4. ↑ Urbanowicz, Ryan J.; Moore, Jason H. (2009-09-22). "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap" (in en). Journal of Artificial Evolution and Applications 2009: 1–25. doi:10.1155/2009/736398. ISSN 1687-6229.
5. ↑ Zhang, C. and Zhang, S., 2002. Association rule mining: models and algorithms. Springer-Verlag.
6. ↑ De Castro, Leandro Nunes, and Jonathan Timmis. Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media, 2002.
7. ↑ ISBN 978-0-7923-1472-1.
8. ↑ Agah, Arvin (2013) (in en). Medical Applications of Artificial Intelligence. CRC Press. ISBN 9781439884331. https://books.google.com/books?id=nWVmAQAAQBAJ&dq=Repeated+Incremental+Pruning+to+Produce+Error+Reduction&pg=PA37. Retrieved 13 August 2017.

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