loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Author: Sang-Woon Kim

Affiliation: Myongji University, Korea, Republic of

ISBN: 978-989-674-021-4

Keyword(s): Dissimilarity-based Classifications, Dimensionality reduction schemes, Prototype selection methods.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: One problem of dissimilarity-based classifications (DBCs) is the high dimensionality of dissimilarity matrices. To address this problem, two kinds of solutions have been proposed in the literature: prototype selection (PS) based methods and dimensionality reduction (DR) based methods. The DR-based method consists of building the dissimilarity matrices using all the available training samples and subsequently applying some of the standard DR schemes. On the other hand, the PS-based method works by directly choosing a small set of representatives from the training samples. Although DR-based and PS-based methods have been explored separately by many researchers, not much analysis has been done on the study of comparing the two. Therefore, this paper aims to find a suitable method for optimizing DBCs by a comparative study. In the experiments, four DR and four PS methods are used to reduce the dimensionality of the dissimilarity matrices, and classification accuracies of the resultant DBC s trained with two real-life benchmark databases are analyzed. Our empirical evaluation on the two approaches demonstrates that the DR-based method can improve the classification accuracies more than the PS-based method. Especially, the experimental results show that the DR-based method is clearly more useful for nonparametric classifiers, but not for parametric ones. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.173.234.237

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kim S. and (2010). ON REDUCING DIMENSIONALITY OF DISSIMILARITY MATRICES FOR OPTIMIZING DBC - An Experimental Comparison.In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 235-240. DOI: 10.5220/0002713002350240

@conference{icaart10,
author={Sang{-}Woon Kim},
title={ON REDUCING DIMENSIONALITY OF DISSIMILARITY MATRICES FOR OPTIMIZING DBC - An Experimental Comparison},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={235-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002713002350240},
isbn={978-989-674-021-4},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - ON REDUCING DIMENSIONALITY OF DISSIMILARITY MATRICES FOR OPTIMIZING DBC - An Experimental Comparison
SN - 978-989-674-021-4
AU - Kim, S.
PY - 2010
SP - 235
EP - 240
DO - 10.5220/0002713002350240

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.