In this paper, we present two fast learning complex-valued, single hidden layer neural
network classifiers namely,'bilinear branch-cut complex-valued extreme learning machine …

While much research has focused on methods for evaluating and maximizing the accuracy
of classifiers either individually or in ensembles, little effort has been devoted to analyzing …

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Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality,
but it has its own drawbacks. Most of the synthetic pattern generation techniques take more …

Dataset generators are useful for the evaluation of an algorithm's performance because they
allow control of the characteristics and amount of data used for benchmarking. We propose …

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 …

Evaluation of classifier performance is often based on statistical methods eg cross-validation
tests. In these tests performance is often strongly related to or solely based on the accuracy …

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 …

Ensemble methods improve the classification accuracy at the expense of testing complexity,
resulting in increased computational costs in real-world applications. Developing a utility …