Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evalua...

Given a noisy dataset, how to locate erroneous instances and attributes and rank suspicious instances based on their impacts on the system performance is an interesting and important research issue. We provide in this paper an Error Detection and Impact-sensitive instance Ranking (EDIR) mechanism to address this problem. Given a noisy dataset D, we first train a benchmark classifier T from D. T...

The paper presents a series of noise detection experiments in a medical problem of coronary artery disease diagnosis. The following algorithms for noise detection and elimination are tested: a saturation lter, a classiication l-ter, a combined classiication-saturation l-ter, and a consensus saturation lter. The distinguishing feature of the novel consensus saturation lter is its high reliabilit...

Background: Hemophilia B is an X-linked recessive coagulation disorder caused by factor IX deficiency.  Analysis of factor IX gene polymorphisms is considered the best approach for prenatal diagnosis and carrier detection of hemophilia B where the identification of gene mutation is not easily possible. Objective: To study the frequency of three factor IX-linked restriction fragment length polym...

A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of th...