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Communications of the IBIMA
Distinctive Feature Extraction for Fast and Reliable Classification in Complex Systems
Seyed Shahrestani
School of Computing and Mathematics, University of Western Sydney, Australia
Volume 2011 (2011), Article ID124938, Communications of the IBIMA, 8 pages
DOI: 10.5171/2011.124938
Copyright © 2011 Seyed Shahrestani.
This is an open access article distributed under the Creative Commons
Attribution License unported 3.0, which permits unrestricted use,
distribution, and reproduction in any medium, provided that original
work is properly cited.
Abstract
In
this work, an approach for establishment of class membership in complex
systems is reported. The classification is based on
adaptive recognition facilitating the discovery of pattern features
that make them distinct from objects belonging to different classes. By
viewing a pattern as a representation of extracts of information
regarding various features of an object, most traditional recognition
methods tend to achieve categorization by identifying the resemblances
amongst the class members. In this work, a different view of
classification is presented. The classification is based on
identification of distinctive features of patterns. It argues that the
basic functioning of the established methods also implies that the
members of different classes have different values for some or all of
such features expressing the objects under consideration. That is, the
categorization can also be based on recognition of dissimilarities and
distinctions between the objects fitting in different classes. Our
proposed approach in based on identifying such charactering
dissimilarities, which will then form the distinctive features of
patterns and objects. In other words, objects are classified as members
of a particular class if they possess some features, which make them
distinguished from other objects present in the universe of objects.
The proposed approach and its language work in a general manner.
Consequently, the corresponding codes can be developed and utilized as
a general adaptive pattern recognition scheme. The generality of the
approach proposed in this work, makes it applicable to many
classification and pattern recognition problems encountered in complex
systems.
Keywords: - Adaptive Recognition, Distinctive Features, Knowledge Base, Negative Recognition
Keywords: - Adaptive Recognition, Distinctive Features, Knowledge Base, Negative Recognition
