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
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Artificial Intelligence and Decision Support Systems
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Biomedical Engineering
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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
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Intelligent Control Systems and Optimization
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Knowledge Discovery and Information Retrieval
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Knowledge-Based Systems
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Machine Learning
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Methodologies and Methods
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Neurocomputing
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Neurotechnology, Electronics and Informatics
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Pattern Recognition
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Physiological Computing Systems
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Sensor Networks
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Soft Computing
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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.
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