人工免疫分类系统AIRS中的非欧几里得距离测量

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI:10.1109/CEC.2004.1330980

J. S. Hamaker, L. Boggess

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引用次数: 51

摘要

AIRS分类器基于源自资源有限的人工免疫系统的原理，在广泛的分类问题上表现一致。本文探讨了在基本的AIRS算法中加入非欧几里德距离度量的效果，使用了四个众所周知的公开可用的分类问题，这些分类问题具有不同比例的真实、离散和名义特征。

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Non-Euclidean distance measures in AIRS, an artificial immune classification system

The AIRS classifier, based on principles derived from resource limited artificial immune systems, performs consistently well over a broad range of classification problems. This paper explores the effects of adding nonEuclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.

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Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

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