A new fast prototype selection method based on clustering

In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T . This process is known as prototype selection, which is an imp...

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Bibliographic Details
Published inPattern analysis and applications : PAA Vol. 13; no. 2; pp. 131 - 141
Main Authors Olvera-López, J. Arturo, Carrasco-Ochoa, J. Ariel, Martínez-Trinidad, J. Francisco
Format Journal Article
LanguageEnglish
Published London Springer-Verlag 01.05.2010
Springer
Subjects
Applied sciences
Computer Science
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Memory organisation. Data processing
Pattern Recognition
Software
Theoretical Advances
Prototype selection
Instance-based classifiers
Clustering
Supervised classification
Border prototypes
Data reduction
Nearest neighbour
Supervised learning
Very large databases
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Summary:In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T . This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-008-0142-x