In this paper, we address the problem of integrating new classes on the fly into on-line
classification systems. The main focus is on visual inspection tasks, although the concepts …

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In pattern recognition, feature selection is a very important task for supervised classification.
The problem consists in, given a dataset where each object is described by a set of features …

This paper describes some of the work we have done to evaluate and compare the use of
three IR systems (Verity, LSI, and SMART) as black boxes within an automated classification …

The purpose of feature construction is to create new higher-level features from original ones.
Genetic Programming (GP) was usually employed to perform feature construction tasks due …

Conventional approaches to combining classifiers improve accuracy at the cost of increased
processing. We propose a novel search based approach to automatically combine multiple …

This paper proposes a novel feature extraction method based on ensemble learning. Using
the error-correcting output codes (ECOC) to design binary classifiers (dichotomizers) for …

A global optimization method is introduced that minimize the rate of misclassification. We
first derive the theoretical basis for the method, on which we base the development of a …

More than a decade ago, combining multiple classifiers was proposed as a possible solution
to the problems posed by the traditional pattern classification approach which involved …