Diffusion ensemble classifiers

Alon Schclar, Lior Rokach, Amir Amit

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We present a novel approach for the construction of ensemble classifiers based on the Diffusion Maps (DM) dimensionality reduction algorithm. The DM algorithm embeds data into a low-dimensional space according to the connectivity between every pair of points in the ambient space. The ensemble members are trained based on dimension-reduced versions of the training set. These versions are obtained by applying the DM algorithm to the original training set using different values of the input parameter. In order to classify a test sample, it is first embedded into the dimension reduced space of each individual classifier by using the Nyström out-of-sample extension algorithm. Each ensemble member is then applied to the embedded sample and the classification is obtained according to a voting scheme. A comparison is made with the base classifier which does not incorporate dimensionality reduction. The results obtained by the proposed algorithms improve on average the results obtained by the non-ensemble classifier.

Original languageEnglish
Title of host publicationIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Pages443-450
Number of pages8
StatePublished - 1 Dec 2012
Event4th International Joint Conference on Computational Intelligence, IJCCI 2012 - Barcelona, Spain
Duration: 5 Oct 20127 Oct 2012

Publication series

NameIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence

Conference

Conference4th International Joint Conference on Computational Intelligence, IJCCI 2012
Country/TerritorySpain
CityBarcelona
Period5/10/127/10/12

Keywords

  • Diffusion Maps
  • Dimensionality Reduction
  • Ensemble Classifiers
  • Nyström Extension
  • Out-of-Sample Extension

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