Feature subset-selection has emerged as a useful technique for creating diversity in
ensembles—particularly in classification ensembles. In this paper we argue that this …

Rotation Forest is a recently proposed method for building classifier ensembles using
independently trained decision trees. It was found to be more accurate than bagging …

It is well known that ensembles of predictors produce better accuracy than a single predictor
provided there is diversity in the ensemble. This diversity manifests itself as disagreement or …

In this paper, we address the automated tuning of input specification for supervised inductive
learning and develop combinatorial optimization solutions for two such tuning problems …

Feature subset selection is an important problem in knowledge discovery, not only for the
insight gained from determining relevant modeling variables, but also for the improved …

Digital mammography is one of the most suitable methods for early detection of breast
cancer. It uses digital mammograms to find suspicious areas containing benign and …

Most EEG-based BCI systems make use of well-studied patterns of brain activity. However,
those systems involve tasks that indirectly map to simple binary commands such as" yes" or" …

OBJECTIVES: The aims of the present study were to evaluate the accuracy of an elaborated
automated voice categorization system that classified voice signal samples into healthy and …

A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the
relevant features in order to focus the learning search. A relaxed setting for Feature …

Ensembles of classifiers have proved to be more effective than a single classification
algorithm in skin image classification problems. Generally, the ensembles are created using …