Diffusion Ensemble Classifiers

Alon Schclar, Lior Rokach, Amir Amit


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.


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Paper Citation

in Harvard Style

Schclar A., Rokach L. and Amit A. (2012). Diffusion Ensemble Classifiers . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 443-450. DOI: 10.5220/0004102804430450

in Bibtex Style

author={Alon Schclar and Lior Rokach and Amir Amit},
title={Diffusion Ensemble Classifiers},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},

in EndNote Style

JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Diffusion Ensemble Classifiers
SN - 978-989-8565-33-4
AU - Schclar A.
AU - Rokach L.
AU - Amit A.
PY - 2012
SP - 443
EP - 450
DO - 10.5220/0004102804430450