A description classifier organizes concepts and relations into a taxonomy based on the results of subsumption computations applied to pairs of relation definitions. Until now, description classifiers have only been designed to operate over definitions phrased in highly restricted subsets of the predicate calculus. This paper describes a classifier able to reason with definitions phrased in the ...

To successfully translate genomic classifiers to the clinical practice, it is essential to obtain reliable and reproducible measurement of the classifier performance. A point estimate of the classifier performance has to be accompanied with a measure of its uncertainty. In general, this uncertainty arises from both the finite size of the training set and the finite size of the testing set. The ...

Feature selection method for text classification based on information gain ranking, improved by removing redundant terms using mutual information measure and inclusion index, is proposed. We report an experiment to study the impact of term redundancy on the performance of text classifier. The result shows that term redundancy behaves very similar to noise and may degrade the classifier performa...

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is show...

In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting class...

A Meta Classifier in this approach is used for the approval of Loan application, as a Data mining classification tool to support Business operations in a very secure way. The goal of designing a Meta classifier system is to achieve the best possible classification performance for the task of effective decision making. This Meta classifier is the combination of Naïve Bayesian classifier, K-Neare...

OBJECTIVE In a previous study, we investigated a sentence classification model that uses semantic features to extract clinically useful sentences from UpToDate, a synthesized clinical evidence resource. In the present study, we assess the generalizability of the sentence classifier to Medline abstracts. METHODS We applied the classification model to an independent gold standard of high qualit...

We introduce the implicitly constrained least squares (ICLS) classifier, a novel semi-supervised version of the least squares classifier. This classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semisupervised methods, this approach does not introduce explicit additional assump...

In this work, we investigate the use of error-correcting output codes (ECOC) for boosting centroid text classifier. The implementation framework is to decompose one multi-class problem into multiple binary problems and then learn the individual binary classification problems by centroid classifier. However, this kind of decomposition incurs considerable bias for centroid classifier, which resul...

Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...