# Rule Induction

@inproceedings{GrzymalaBusse2005RuleI, title={Rule Induction}, author={Jerzy W. Grzymala-Busse}, booktitle={Data Mining and Knowledge Discovery Handbook}, year={2005} }

This chapter begins with a brief discussion of some problems associated with input data. Then different rule types are defined. Three representative rule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed.

#### 35 Citations

Binary classification rule generation from decomposed data

- Computer Science
- Int. J. Intell. Syst.
- 2019

It is proven that for training data, the quality of the rule set generated using the approach is the same as that for the whole data, and experimentally verified that for test data,The quality of classification is comparable with that obtained using a nondecomposed data approach. Expand

Bootstrapping rule induction

- Computer Science
- Third IEEE International Conference on Data Mining
- 2003

This work describes a method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst and to provide measures of variance to continuous attribute decision boundaries and accuracy-point estimates. Expand

Rule Induction on Data Sets with Set-Value Attributes

- Mathematics
- 2017

Changing the creation of characteristic sets and attribute-value blocks to include all values for each case allows LEM2 to induce rules on data sets with set-value attributes. Expand

Multi-target regression with rule ensembles

- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 2012

The FIRE algorithm for solving multi-target regression problems is introduced, which employs the rule ensembles approach and is significantly more concise than random forests, and it is also possible to create compact rule sets that are smaller than a single regression tree but still comparable in accuracy. Expand

Novel approach to data discretization

- Mathematics, Engineering
- Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA)
- 2015

Discretization is an important preprocessing step in data mining. The data discretization method involves determining the ranges of values for numeric attributes, which ultimately represent discrete… Expand

Bootstrapping rule induction to achieve rule stability and reduction

- Computer Science
- Journal of Intelligent Information Systems
- 2006

A method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst, to measure and increase the stability of the rule induction process, and to provide a measure of variance to continuous attribute decision boundaries and accuracy point estimates is described. Expand

Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

- Computer Science
- Complex.
- 2018

A new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Expand

Rough Sets and Rule Induction in Imperfect Information Systems

- Computer Science
- 2014

This paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching, which is then used to define valued tolerance/similarity relations and to develop new rough set models based on the valued tolerance and similarity relations. Expand

Automatic Classification of the ¡§De¡¨ Word Usage for Chinese as a Foreign Language

- Computer Science
- Int. J. Comput. Linguistics Chin. Lang. Process.
- 2015

According to the experimental results, the proposed method can provide good enough performance to classify the character usages for morphosyntatic particle ＂De, and obtains a significant improvement in character selection. Expand

A CRITICAL EXPLORATION OF THE POTENTIAL UTILITY OF RULE INDUCTION DATA MINING METHODS TO “ORTHODOX” EDUCATION RESEARCH

- 2018

Despite some theoretical promise, it is unclear whether rule induction data mining approaches (e.g., classification trees and association rules) add methodological value to "orthodox" education… Expand

#### References

SHOWING 1-10 OF 60 REFERENCES

LERS-A System for Learning from Examples Based on Rough Sets

- Computer Science
- Intelligent Decision Support
- 1992

The paper presents the system LERS for rule induction, which handles inconsistencies in the input data due to its usage of rough set theory principle and induces all rules, each in the minimal form, that can be induced from the inputData. Expand

Classification of Individuals with Complex Structure

- Computer Science
- ICML
- 2000

A systematic individuals-as-terms approach to knowledge representation for inductive learning based on the use of higher-order logic for knowledge representation is provided and the utility of types and higher- order constructs for this purpose is demonstrated. Expand

Principled Constructive Induction

- Computer Science
- IJCAI
- 1989

It is shown how the proposed framework can be used to combine techniques for selection of representative examples with techniques for construction of new features, in order to solve difficult problems in learning from examples. Expand

The CN2 Induction Algorithm

- Computer Science
- Machine Learning
- 2004

A description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Expand

Using the m -estimate in rule induction

- Computer Science
- 1993

This work replaces the Laplace estimate in the rule induction system CN2 with a general Bayesian probability estimate, the m- estimate, which does not rely on the Laplacian assumption of equally likely classes and allows for adapting to the learning domain. Expand

Learning from Imbalanced Data Sets: A Comparison of Various Strategies *

- Mathematics
- 2000

Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exists many… Expand

Knowledge acquisition under uncertainty — a rough set approach

- Mathematics, Computer Science
- J. Intell. Robotic Syst.
- 1988

The paper describes knowledge acquisition under uncertainty using rough set theory, a concept introduced by Z. Pawlak in 1981, and shows that some classifications are theoretically (and, therefore, in practice) forbidden. Expand

A New Version of the Rule Induction System LERS

- Computer Science
- Fundam. Informaticae
- 1997

A new version of the rule induction system LERS is described and compared with the old version and the new LERS system performance is fully comparable with performance of the other two systems. Expand

Fast Eeective Rule Induction

- Computer Science
- 1995

This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modiications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more eecient on large samples. Expand

Rule Induction with CN2: Some Recent Improvements

- Mathematics, Computer Science
- EWSL
- 1991

Improvements to the CN2 algorithm are described, including the use of the Laplacian error estimate as an alternative evaluation function and it is shown how unordered as well as ordered rules can be generated. Expand