Feature ranking methods used for selection of prototypes

Authors:
Marcin Blachnik;Włodzisław Duch;Tomasz Maszczyk
Affiliations:
Dept. of Management & Informatics, Silesian University of Technology, Katowice, Poland;Department of Informatics, Nicolaus Copernicus University, Toruń, Poland,School of Computer Engineering, Nanyang Technological University, Singapore;Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

Citing 5
Cited 0

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Abstract

Prototype selection, as a preprocessing step in machine learning, is effective in decreasing the computational cost of classification task by reducing the number of retained instances. This goal is obtained by shrinking the level of noise and rejecting the irrelevant data. Prototypes may be also used to understand the data through improving comprehensibility of results. In the paper we discus an approach for instance selection based on techniques known from feature selection pointing out the dualism between feature and instance selection. Finally some experiments are shown which uses feature ranking methods for instance selection.