When several classifiers are brought to contribute to the same task of recognition, various
strategies of decisions, implying these classifiers in different ways, are possible. A first …

We introduce a new way of describing the diversity of an ensemble of classifiers, the
Percentage Correct Diversity Measure, and compare it against existing methods. We then …

It has been accepted that multiple classifier systems provide a platform for not only
performance improvement, but more efficient and robust pattern classification systems. A …

Classifier subset selection (CSS) from a large ensemble is an effective way to design
multiple classifier systems (MCSs). Given a validation dataset and a selection criterion, the …

We define two simple probability models of dependency between classifiers: a multiplicative
form and an additive form. This paper explores the relationship between the majority vote …

Researchers continue to focus on the design of pattern recognition systems to achieve the
best classification rates. Usually, different classification schemes are developed for the …

There is a common agreement in many recent publications related to pattern recognition that
dependency among classifier outputs plays a key role in combining classifiers [1]–[7] …

The recent trend of combining internet with cellular phone gives an important chance to
mobile telecommunication industry. It is mainly due to the fact that the mobile internet service …

The current generation of Earth Observation sensors are producing data with high potential
for use in scientific and technological investigations. Moreover, the complexity and capture …

In this paper, the application of Multiple Classifier Systems and Soft Computing techniques
to the classification of Bronze Age axes found in Italian territory is shown. The methodology …