@inproceedings{7e3e518e60f348aea4ddf1dba24cc039,
title = "Diffusion ensemble classifiers",
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 Nystr{\"o}m 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.",
keywords = "Diffusion Maps, Dimensionality Reduction, Ensemble Classifiers, Nystr{\"o}m Extension, Out-of-Sample Extension",
author = "Alon Schclar and Lior Rokach and Amir Amit",
year = "2012",
month = dec,
day = "1",
language = "English",
isbn = "9789898565334",
series = "IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence",
pages = "443--450",
booktitle = "IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence",
note = "4th International Joint Conference on Computational Intelligence, IJCCI 2012 ; Conference date: 05-10-2012 Through 07-10-2012",
}