Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System

Bacardit, Jaume; Guiu, Josep Maria Garrell i

doi:10.1007/3-540-36131-6_36

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Recent methods based on evolutionary algorithms performing multivariate discretization during the learning process are [29], [38], and [39], where the evolutionary algorithms for classification GIL [40] and GABIL [41] are extended into the systems EDRL-MD and GAssit, respectively. In both EDRL-MD and GAssist, an individual encodes a set of rules, and continuous attributes are handled by means of inequalities that can be modified during the evolutionary process.…”

Section: Related Workmentioning

confidence: 99%

Handling Continuous Attributes in an Evolutionary Inductive Learner

Divina

Marchiori

2005

IEEE Trans. Evol. Computat.

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This paper analyzes experimentally discretization algorithms for handling continuous attributes in evolutionary learning. We consider a learning system that induces a set of rules in a fragment of first-order logic (evolutionary inductive logic programming), and introduce a method where a given discretization algorithm is used to generate initial inequalities, which describe subranges of attributes' values. Mutation operators exploiting information on the class label of the examples (supervised discretization) are used during the learning process for refining inequalities. The evolutionary learning system is used as a platform for testing experimentally four algorithms: two variants of the proposed method, a popular supervised discretization algorithm applied prior to induction, and a discretization method which does not use information on the class labels of the examples (unsupervised discretization). Results of experiments conducted on artificial and real life datasets suggest that the proposed method provides an effective and robust technique for handling continuous attributes by means of inequalities.

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Section: Related Workmentioning

confidence: 99%

Handling Continuous Attributes in an Evolutionary Inductive Learner

Divina

Marchiori

2005

IEEE Trans. Evol. Computat.

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“…In [16] a variant of the Fayyad and Irani's method is used for discretizing numerical attributes in an Inductive Logic Programming system. In [1,2] methods using adaptive discrete intervals are used within a GA based system for classification. Another approach for local discretization is proposed by Kwedlo and Kretowski.…”

Section: Introductionmentioning

confidence: 99%

A Method for Handling Numerical Attributes in GA-Based Inductive Concept Learners

Divina

Keijzer

Marchiori

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. This paper proposes a method for dealing with numerical attributes in inductive concept learning systems based on genetic algorithms. The method uses constraints for restricting the range of values of the attributes and novel stochastic operators for modifying the constraints. These operators exploit information on the distribution of the values of an attribute. The method is embedded into a GA based system for inductive logic programming. Results of experiments on various data sets indicate that the method provides an effective local discretization tool for GA based inductive concept learners.

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The Role of Interval Initialization in a GBML System with Rule Representation and Adaptive Discrete Intervals

Bacardit

Guiu

2002

Lecture Notes in Computer Science

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Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System

Cited by 8 publications

References 5 publications

Handling Continuous Attributes in an Evolutionary Inductive Learner

Handling Continuous Attributes in an Evolutionary Inductive Learner

A Method for Handling Numerical Attributes in GA-Based Inductive Concept Learners

The Role of Interval Initialization in a GBML System with Rule Representation and Adaptive Discrete Intervals

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