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

被引：17

作者：

Hamaker, JS ^{[1
]}

Boggess, L ^{[1
]}

机构：

[1] Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA

来源：

CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 | 2004年

关键词：

D O I：

10.1109/CEC.2004.1330980

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 non-Euclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.

引用

页码：1067 / 1073

页数：7

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