language: en

[Proceedings of the International Conference on Artificial Intelligence, Expert Systems and Neural Networks (AIE96), pp. 11-14, 1996.]

Instance-Based Learning with Genetically Derived Attribute Weights

D. Randall Wilson, Tony R. Martinez
e-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu
Computer Science Department, Brigham Young University,
Provo, UT 84602, U.S.A.
Key words: instance-based learning, genetic algorithms, instance weights, generalization

Abstract. This paper presents an inductive learning system called the Genetic Instance-Based Learning (GIBL) system. This system combines instance-based learning approaches with evolutionary computation in order to achieve high accuracy in the presence of irrelevant or redundant attributes. Evolutionary computation is used to find a set of attribute weights that yields a high estimate of classification accuracy. Results of experiments on 16 data sets are shown, and are compared with a non-weighted version of the instance-based learning system. The results indicate that the generalization accuracy of GIBL is somewhat higher than that of the non-weighted system on regular data, and is significantly higher on data with irrelevant or redundant attributes.

1. Introduction