An Empirical Analysis of Action Map in Learning Classifier Systems
An action map is one of the most fundamental options in designing a learning classifier system (LCS), which defines how LCSs cover a state action space in a problem. It still remains unclear which action map can be adequate to solve which type of problem effectively, resulting in a lack of basic des...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-05-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.9746/jcmsi.11.239 |
| _version_ | 1797661009855381504 |
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| author | Masaya Nakata Keiki Takadama |
| author_facet | Masaya Nakata Keiki Takadama |
| author_sort | Masaya Nakata |
| collection | DOAJ |
| description | An action map is one of the most fundamental options in designing a learning classifier system (LCS), which defines how LCSs cover a state action space in a problem. It still remains unclear which action map can be adequate to solve which type of problem effectively, resulting in a lack of basic design methodology of LCS in terms of the action map. This paper attempts to empirically conclude this issue with an intensive analysis comparing different action maps on LCSs. From the analysis on a benchmark classification problem, we identify a fact that an adequate action map can be determined depending on a type of problem difficulty such as class imbalance, more generally, a complexity of classification or decision boundary of problem. We also conduct an experiment on a human activity recognition task as a real world classification problem, and then confirm that a suggested adequate action map from the analysis enables an LCS to improve on the performance. Those results claim that the action map should be selected adequately in designing LCSs in order to improve their potential performance. |
| first_indexed | 2024-03-11T18:39:12Z |
| format | Article |
| id | doaj.art-d47a9f6a5503401cb6fcb81397620d1a |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:39:12Z |
| publishDate | 2018-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-d47a9f6a5503401cb6fcb81397620d1a2023-10-12T13:43:55ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702018-05-0111323924810.9746/jcmsi.11.23912103213An Empirical Analysis of Action Map in Learning Classifier SystemsMasaya Nakata0Keiki Takadama1Graduate School of Engineering, Yokohama National UniversityGraduate School of Informatics, The University of Electro-CommunicationsAn action map is one of the most fundamental options in designing a learning classifier system (LCS), which defines how LCSs cover a state action space in a problem. It still remains unclear which action map can be adequate to solve which type of problem effectively, resulting in a lack of basic design methodology of LCS in terms of the action map. This paper attempts to empirically conclude this issue with an intensive analysis comparing different action maps on LCSs. From the analysis on a benchmark classification problem, we identify a fact that an adequate action map can be determined depending on a type of problem difficulty such as class imbalance, more generally, a complexity of classification or decision boundary of problem. We also conduct an experiment on a human activity recognition task as a real world classification problem, and then confirm that a suggested adequate action map from the analysis enables an LCS to improve on the performance. Those results claim that the action map should be selected adequately in designing LCSs in order to improve their potential performance.http://dx.doi.org/10.9746/jcmsi.11.239learning classifier systemaction mapperformance analysisevolutionary computationclassification |
| spellingShingle | Masaya Nakata Keiki Takadama An Empirical Analysis of Action Map in Learning Classifier Systems SICE Journal of Control, Measurement, and System Integration learning classifier system action map performance analysis evolutionary computation classification |
| title | An Empirical Analysis of Action Map in Learning Classifier Systems |
| title_full | An Empirical Analysis of Action Map in Learning Classifier Systems |
| title_fullStr | An Empirical Analysis of Action Map in Learning Classifier Systems |
| title_full_unstemmed | An Empirical Analysis of Action Map in Learning Classifier Systems |
| title_short | An Empirical Analysis of Action Map in Learning Classifier Systems |
| title_sort | empirical analysis of action map in learning classifier systems |
| topic | learning classifier system action map performance analysis evolutionary computation classification |
| url | http://dx.doi.org/10.9746/jcmsi.11.239 |
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