Instance Selection Algorithm based on a Ranking Procedure

被引：0

作者：

Pereira, Cristiano de Santana ^{[1
]}

Cavalcanti, George D. C. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil

来源：

2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2011年

关键词：

SELF-GENERATING PROTOTYPES; NEAREST-NEIGHBOR RULE; PATTERN-CLASSIFICATION; LEARNING ALGORITHMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.

引用

页码：2409 / 2416

页数：8

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