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 …

Ensemble approaches to classification and regression have attracted a great deal of interest
in recent years. These methods can be shown both theoretically and empirically to …

Individual classification models are recently challenged by combined pattern recognition
systems, which often show better performance. In such systems the optimal set of classifiers …

Ensembles of learning machines constitute one of the main current directions in machine
learning research, and have been applied to a wide range of real problems. Despite of the …

When more than a single classifier has been trained for the same recognition problem the
question arises how this set of classifiers may be combined into a final decision rule. Several …

The diversity of an ensemble of classifiers can be calculated in a variety of ways. Here a
diversity metric and a means for altering the diversity of an ensemble, called “thinning”, are …

We study the issue of error diversity in ensembles of neural networks. In ensembles of
regression estimators, the measurement of diversity can be formalised as the Bias-Variance …

Multiple classifier systems (MCSs) based on the combination of outputs of a set of different
classifiers have been proposed in the field of pattern recognition as a method for the …

In the field of pattern recognition, multiple classifier systems based on the combination of
outputs of a set of different classifiers have been proposed as a method for the development …

Protein function prediction is a complex multiclass multilabel classification problem,
characterized by multiple issues such as the incompleteness of the available annotations …