Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system

Jaume Bacardit; Josep Maria Garrell

doi:10.1007/978-3-540-71231-2_5

Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system

Jaume Bacardit^*
, Josep Maria Garrell

^*Corresponding author for this work

Research output: Book chapter › Conference contribution › peer-review

45 Citations (Scopus)

Abstract

Bloat control and generalization pressure are very important issues in the design of Pittsburgh Approach Learning Classifier Systems (LCS), in order to achieve simple and accurate solutions in a reasonable time. In this paper we propose a method to achieve these objectives based on the Minimum Description Length (MDL) principle. This principle is a metric which combines in a smart way the accuracy and the complexity of a theory (rule set , instance set, etc.). An extensive comparison with our previous generalization pressure method across several domains and using two knowledge representations has been done. The test show that the MDL based size control method is a good and robust choice.

Original language	English
Title of host publication	Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers
Publisher	Springer Verlag
Pages	59-79
Number of pages	21
ISBN (Print)	9783540712305
DOIs	https://doi.org/10.1007/978-3-540-71231-2_5
Publication status	Published - 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4399 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-71231-2_5

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Bacardit, J., & Garrell, J. M. (2007). Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system. In Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers (pp. 59-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4399 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-71231-2_5

Bacardit, Jaume ; Garrell, Josep Maria. / Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system. Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers. Springer Verlag, 2007. pp. 59-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system",

abstract = "Bloat control and generalization pressure are very important issues in the design of Pittsburgh Approach Learning Classifier Systems (LCS), in order to achieve simple and accurate solutions in a reasonable time. In this paper we propose a method to achieve these objectives based on the Minimum Description Length (MDL) principle. This principle is a metric which combines in a smart way the accuracy and the complexity of a theory (rule set , instance set, etc.). An extensive comparison with our previous generalization pressure method across several domains and using two knowledge representations has been done. The test show that the MDL based size control method is a good and robust choice.",

author = "Jaume Bacardit and Garrell, \{Josep Maria\}",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bacardit, J & Garrell, JM 2007, Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system. in Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4399 LNAI, Springer Verlag, pp. 59-79. https://doi.org/10.1007/978-3-540-71231-2_5

Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system. / Bacardit, Jaume; Garrell, Josep Maria.
Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers. Springer Verlag, 2007. p. 59-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4399 LNAI).

Research output: Book chapter › Conference contribution › peer-review

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AU - Garrell, Josep Maria

PY - 2007

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N2 - Bloat control and generalization pressure are very important issues in the design of Pittsburgh Approach Learning Classifier Systems (LCS), in order to achieve simple and accurate solutions in a reasonable time. In this paper we propose a method to achieve these objectives based on the Minimum Description Length (MDL) principle. This principle is a metric which combines in a smart way the accuracy and the complexity of a theory (rule set , instance set, etc.). An extensive comparison with our previous generalization pressure method across several domains and using two knowledge representations has been done. The test show that the MDL based size control method is a good and robust choice.

AB - Bloat control and generalization pressure are very important issues in the design of Pittsburgh Approach Learning Classifier Systems (LCS), in order to achieve simple and accurate solutions in a reasonable time. In this paper we propose a method to achieve these objectives based on the Minimum Description Length (MDL) principle. This principle is a metric which combines in a smart way the accuracy and the complexity of a theory (rule set , instance set, etc.). An extensive comparison with our previous generalization pressure method across several domains and using two knowledge representations has been done. The test show that the MDL based size control method is a good and robust choice.

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SN - 9783540712305

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers

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Bacardit J, Garrell JM. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system. In Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers. Springer Verlag. 2007. p. 59-79. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-71231-2_5

Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system

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