loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Sang-Woon Kim

Affiliation: Myongji University, Korea, Republic of

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 DB Cs 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)

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 44.222.146.114

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. (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; ISSN 2184-433X, SciTePress, 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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Kim, S.
PY - 2010
SP - 235
EP - 240
DO - 10.5220/0002713002350240
PB - SciTePress