Homogeneous bipartition based on multidimensional ranking

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We present an algorithm which partitions a data set in two parts with equal size and experimentally nearly the same distribution measured through the likelihood of a Parzen kernel density estimator. The generation of the partition takes O(1\2N(N - 1)) operations (N number of data) and is 2 orders of magnitude faster than the state of the art.

Original languageEnglish
Title of host publicationESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages259-264
Number of pages6
Publication statusPublished - 1 Dec 2008
Event16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008 - Bruges, Belgium
Duration: 23 Apr 200825 Apr 2008

Publication series

NameESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Other

Other16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008
CountryBelgium
CityBruges
Period23/4/0825/4/08

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Aupetit, M. (2008). Homogeneous bipartition based on multidimensional ranking. In ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 259-264). (ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning).