Authors:
Alon Schclar
1
;
Lior Rokach
2
and
Amir Amit
3
Affiliations:
1
Academic College of Tel Aviv-Yaffo, Israel
;
2
Ben-Gurion University of the Negev, Israel
;
3
Interdisciplinary Center (IDC) Herzliya, Israel
Keyword(s):
Ensemble Classifiers, Dimensionality Reduction, Out-of-Sample Extension, Diffusion Maps, Nyström Extension.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
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.
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