IPRed: Instance reduction algorithm based on the percentile of the partitions

Turki Turki, Zhi Wei

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Instance reduction methods are popular methods that reduce the size of the datasets to possibly improve the classification accuracy. We present a method that reduces the size of the dataset based on the percentile of the dataset partitions which we call IPRed. We evaluate our and other popular instance reduction methods from a classification perspective by 1-nearest neighbor algorithm on many real datasets. Our experimental evaluation on the datasets shows that our method yields the minimum average error with statistical significance.

Original languageEnglish (US)
Pages (from-to)181-185
Number of pages5
JournalCEUR Workshop Proceedings
Volume1353
StatePublished - 2015
Event26th Modern Artificial Intelligence and Cognitive Science Conference, MAICS 2015 - Greensboro, United States
Duration: Apr 25 2015Apr 26 2015

All Science Journal Classification (ASJC) codes

  • General Computer Science

Keywords

  • Classification
  • Instance reduction
  • Nearest neighbor
  • Statistical significance

Cite this