A Survey on Evolutionary Instance Selection and Generation

View Sample PDF

Author(s): Joaquín Derrac (University of Granada, Spain), Salvador García (University of Jaén, Spain)and Francisco Herrera (University of Granada, Spain)
Copyright: 2012
Pages: 34
Source title: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends
Source Author(s)/Editor(s): Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/978-1-4666-0270-0.ch014

Keywords: Algorithms / Artificial Intelligence / Computer Science & IT / Information Science Reference

Purchase

View A Survey on Evolutionary Instance Selection and Generation on the publisher's website for pricing and purchasing information.

Abstract

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

The IRMA Community

Research IRM

A Survey on Evolutionary Instance Selection and Generation

Purchase

Abstract

Related Content

IRMA Sponsors