A Hybrid Genetic Algorithm for Classification
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 645-649.
Abstract
In this paper we describe a method for hybridizing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algorithm to classify new data records based on their weighted distance from the members of the training set. We applied our hybrid algorithm to three test cases. Oassification results obtained with the hybrid algorithm exceed the perfonnance of the k nearest neighbors algorithm in all three cases.
@inproceedings{ijcai1991p94,
title = {A Hybrid Genetic Algorithm for Classification},
author = {Jr., James D. Kelly and Davis, Lawrence},
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 = {645--649},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/94},
}
TY - CPAPER TI - A Hybrid Genetic Algorithm for Classification AU - Jr., James D. Kelly AU - Davis, Lawrence T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 645 EP - 649 UR - https://ijcai.org/proceedings/1991/94 ER -Copied
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