Ensemble models have been extensively applied to credit scoring. However, advanced tree-
based classifiers have been seldom utilized as components of ensemble models. Moreover …

In this paper, we propose a multi-category classification method that combines binary
classifiers through soft-max function. Posteriori probabilities are also obtained. Both, one …

The classification and the count of white blood cells in microscopy images allows the in vivo
assessment of a wide range of important hematic pathologies (ie, from presence of …

DESlib is an open-source python library providing the implementation of several dynamic
selection techniques. The library is divided into three modules:(i) dcs, containing the …

In real life, classifier learning may encounter a dataset in which the number of instances of a
given class is much higher than for other classes. Such imbalanced datasets require special …

We have previously introduced an incremental learning algorithm Learn++, which learns
novel information from consecutive data sets by generating an ensemble of classifiers with …

The Random Forest (RF) classifiers are suitable for dealing with the high dimensional noisy
data in text classification. An RF model comprises a set of decision trees each of which is …

It is well known in the pattern recognition community that the accuracy of classifications
obtained by combining decisions made by independent classifiers can be substantially …

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 …

We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier
system. The first GA version selects disjoint feature subsets to be used by the individual …