Learning Concept Classification Rules Using Genetic Algorithms
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 651-656.
Abstract
In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a system GABIL which continually learns and refines concept classification rules from its interaction with the environment. The performance of the system is measured on a set of concept learning problems and compared with the performance of two existing systems: IDSR and C4.5. Preliminary results support that, despite minimal system bias, GABIL is an effective concept learner and is quite competitive with ID5R and C4.5 as the target concept increases in complexity.
@inproceedings{ijcai1991p95,
title = {Learning Concept Classification Rules Using Genetic Algorithms},
author = {De Jong, Kenneth A. and Spears, William M.},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {651--656},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/95},
}
TY - CPAPER TI - Learning Concept Classification Rules Using Genetic Algorithms AU - De Jong, Kenneth A. AU - Spears, William M. T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 651 EP - 656 UR - https://ijcai.org/proceedings/1991/95 ER -Copied
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