Classification in complex systems through negative recognition
Seyed A. Shahrestani · International Conference on Mathematical methods, Computational techniques and Intelligent systems · 2009
In this work, an approach to establish class membership conditions, using a labelled training set, is described. Most pattern recognition and classification approaches are based on identifying the similarities between the members of each class. In this work, a different view of classification is presented. The classification is based on identification of distinctive features of patterns. It will be shown that the members of different classes have different values for some or all of such features. 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 paper will also show that by making use of the distinctive features and their corresponding values, classification of all patterns, even for complex systems, can be accomplished.