文章基本信息

标题：Artificial Immune Classifier Based on ELLipsoidal Regions (AICELL) †
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作者：Aris Lanaridis ; Giorgos Siolas ; Andreas Stafylopatis 等
期刊名称：Computation
电子版ISSN：2079-3197
出版年度：2017
卷号：5
期号：2
页码：31-54
DOI：10.3390/computation5020031
出版社：MDPI Publishing
摘要：Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes.
关键词：artificial immune systems; artificial immune networks; pattern classification; learning classifier systems; evolutionary algorithms artificial immune systems ; artificial immune networks ; pattern classification ; learning classifier systems ; evolutionary algorithms