Density-Based Evolutionary Outlier Detection

被引：0

作者：

Banerjee, Amit ^{[1
]}

机构：

[1] Penn State Univ, Sch Sci Engn & Technol, Middletown, PA 17057 USA

来源：

PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION COMPANION (GECCO'12) | 2012年

关键词：

Outlier Detection; data density; genetic algorithm; nearest neighbor distance;

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：

A novel density-based distance measure and an outlier detection method using evolutionary search are presented in this paper. A fitness function based on nearest neighbor distances is proposed and the genetic recombination operators are designed to achieve a balance of exploration and exploitation in the nearest neighborhood space. The methodology is tested on datasets of varying sizes (small to moderate) and dimensionalities and performance is compared to existing evolutionary methods for outlier detection.

引用

页码：651 / 652

页数：2

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