Classifiers based on probability density estimates can be used to find posterior probabilities for the objects to be classified. These probabilities can be used for rejection or for combining classifiers. Posterior probabilities for other classifiers, however, have to be conditional for the classifier., i.e. they yield class probabilities for a given value of the classifier outcome instead for ...

Credit card fraud detection based on machine learning has recently attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the fraud class (minority class) despite realizing high overall accuracy. This is due to...

Although at first sight probabilistic networks and fuzzy clustering seem to be disparate areas of research, a closer look reveals that they can both be seen as generalizations of naive Bayes classifiers. If all attributes are numeric (except the class attribute, of course), naive Bayes classifiers often assume an axis-parallel multidimensional normal distribution for each class as the underlyin...

An application of the rule induction algorithm MODLEM to construct multiple classifiers is studied. Two different such classifiers are considered: the bagging approach, where classifiers are generated from different samples of the learning set, and the n-classifier, which is specialized for solving multiple class learning problems. This paper reports results of an experimental comparison of the...

Studies in the area of Pattern Recognition have indicated that a classification model performs differently from class to class. This observation leads to combining the individual results of different classifiers to derive a consensus decision. This work investigates the combination of classifiers in a facial expression recognition system. A classifier ensemble is then built by integrating the r...

MOTIVATION Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies, such as the Gene Ontology (GO). Over a hierarchy of functional classes, a grou...

In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ignore the de...

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Hard and soft classifiers are two important groups of techniques for classification problems. Logistic regression and Support Vector Machines are typical examples of soft and hard classifiers respectively. The essential difference between these two groups is whether one needs to estimate the class conditional probability for the classification task or not. In particular, soft classifiers predic...