InstanceRank: Bringing order to datasets

被引：15

作者：

Vallejo, Carlos G. ^{[1
]}

Troyano, Jose A. ^{[1
]}

Javier Ortega, F. ^{[1
]}

机构：

[1] Univ Seville, Dept Comp Languages & Syst, E-41012 Seville, Spain

来源：

PATTERN RECOGNITION LETTERS | 2010年 / 31卷 / 02期

关键词：

Instance-based learning; Instance reduction; Nearest neighbor; PageRank; Classification; MULTIPLE DATA SETS; LEARNING ALGORITHMS; STATISTICAL COMPARISONS; NEAREST; CLASSIFIERS; ERROR;

D O I：

10.1016/j.patrec.2009.09.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present InstanceRank, a ranking algorithm that reflects the relevance of the instances within a dataset. InstanceRank applies a similar solution to that used by PageRank, the web pages ranking algorithm in the Google search engine. We also present ISR, an instance selection technique that uses InstanceRank. This algorithm chooses the most representative instances from a learning database. Experiments show that ISR algorithm, with InstanceRank as ranking criteria, obtains similar results in accuracy to other instance reduction techniques, noticeably reducing the size of the instance set. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：133 / 142

页数：10

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