Performance and Population State Metrics for Rule-based Learning Systems

T Kovacs

Performance and Population State Metrics for Rule-based Learning Systems

T Kovacs

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the XCS system, and demonstrate their superiority in some cases.

Translated title of the contribution	Performance and Population State Metrics for Rule-based Learning Systems
Original language	English
Title of host publication	Unknown
Editors	David Fogel
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1781 - 1786
Number of pages	5
Publication status	Published - May 2002

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 2002 Congress on Evolutionary Computation (CEC)

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title = "Performance and Population State Metrics for Rule-based Learning Systems",

abstract = "We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the XCS system, and demonstrate their superiority in some cases.",

author = "T Kovacs",

note = "Conference Proceedings/Title of Journal: Proceedings of the 2002 Congress on Evolutionary Computation (CEC)",

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editor = "David Fogel",

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N1 - Conference Proceedings/Title of Journal: Proceedings of the 2002 Congress on Evolutionary Computation (CEC)

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N2 - We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the XCS system, and demonstrate their superiority in some cases.

AB - We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the XCS system, and demonstrate their superiority in some cases.

M3 - Conference Contribution (Conference Proceeding)

SP - 1781

EP - 1786

BT - Unknown

A2 - Fogel, David

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Performance and Population State Metrics for Rule-based Learning Systems

Abstract

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