This may be used to summarise the predictive performance of a classifier on test data. It is commonly encountered in a two-class format, but can be generated for any number of classes. Suppose we have a two-class problem with classes referred to as positive and negative. A single prediction by a classifier can have four outcomes which are displayed in the confusion matrix of Table 1. Predicted ...

A common way to evaluate a multi-way classifier is a confusion matrix that plots, for each of the learned concepts, the true class of test instances against the predicted classes. Aggregate accuracy figures of the classifier are obtained by summing up the diagonal entries of the confusion matrix. However, invaluable information about the relationships amongst classes is often ignored. In this r...

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...

Biometric identity verification systems frequently face the challenges of non-controlled conditions of data acquisition. Under such conditions biometric signals may suffer from quality degradation due to extraneous, identity-independent factors. It has been demonstrated in numerous reports that a degradation of biometric signal quality is a frequent cause of significant deterioration of classif...

A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classi...

Lapata and Brew [8] (hereafter LB04) obtain from untagged texts a statistical prior model that is able to generate class preferences for ambiguous Levin [9] verbs. They also show that their informative priors, incorporated into a Naive Bayesian classifier deduced from handtagged data, can aid in verb class disambiguation. We re-examine the parameter estimation of LB04’s prior model and identify...

Classification methods generally rely on some idea about the data structure. If the specific assumptions are not met, a classifier may fail. In this paper the possibility of combining classifiers in multi-class problems is investigated. Multi-class classification problems are split into two class problems. For each of the latter problems an optimal classifier is determined. The results of apply...

Automatic document classification has become an important task because of the continually increasing number of text documents with the users have to deal with. The aim of this paper is to develop a non-adaptive meta-classifier for text documents that has an increased classification accuracy. The developed meta-classifier is based on combining some SVM classifiers and a Naïve Bayes classifier. W...

This paper is focused on the classification of multidimensional patterns in classes located in a noisy environment using approximation of the class areas through radial basis elements. Different variants of class area approximation are proposed and investigated. An approach for classification in overlapping classes is discussed. It is based on the class areas approximation taking into account t...

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct...