A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Researchers from various disciplines such as pattern recognition, statistics, and machine
learning have explored the use of ensemble methodology since the late seventies. Thus …

Ensemble methods that train multiple learners and then combine them for use, with Boosting
and Bagging as representatives, are a kind of state-of-theart learning approach. It is well …

Broad classes of statistical classification algorithms have been developed and applied
successfully to a wide range of real-world domains. In general, ensuring that the particular …

Over the last couple of decades, multiple classifier systems, also called ensemble systems
have enjoyed growing attention within the computational intelligence and machine learning …

During the 1990's many methods were proposed for combining multiple classifiers for a
single recognition task. With these methods, the focus of the field shifted from the …

In critical applications, such as medical diagnosis, security related systems, and so on, the
cost or risk of action taking based on incorrect classification can be very high. Hence …

Classifier combination is now an established pattern recognition subdiscipline. Despite the
strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and …

For building implementable and industryvaluable classification solutions, machine learning
methods must focus not only on accuracy but also on computational and space complexity …

Feature selection has become an indispensable preprocessing step in an expert system.
Improving the feature selection performance could guide such a system to make better …