A multi-classifier diagnostic system was designed for distinguishing between benign and malignant thyroid nodules from routinely taken (FNA, H&E-stained) cytological images. To construct the multi-classifier system, several combination rules and different mixtures of ensemble classifier members, employing morphological and textural nuclear features, were comparatively evaluated. Experimental re...

Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions ...

We present a framework for characterizing Bayesian classification methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning of full Bayesian networks at the most general extreme. While much work has been carried out along the two ends of this spectrum, there has been ...

This work addresses the problem of having to train a Naïve Bayesian classifier using limited data. It first presents an improved instance-weighting algorithm that is accurate and robust to noise and then it shows how to combine it with a fine tuning algorithm to achieve even better classification accuracy. Our empirical work using 49 benchmark data sets shows that the improved instance-weightin...

This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm. A dual threshold method is applied to determine the diagnosis strategy and suppress false alarm signals. The experimenta...

The development of Bayesian classifiers is frequently accomplished by means of algorithms which are highly data-driven. Often, however, sufficient data are not available, which may be compensated for by eliciting background knowledge from experts. This paper explores the trade-offs between modelling using background knowledge from domain experts and machine learning using a small clinical datas...

It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class probabilities is desired. For example, a ranking of customers in terms of the likelihood that they buy one’s products is useful in direct marketing. What is the general performance of naive Bayes in ranking? In this paper...

In this paper, we propose a generic description of the concept lattice as classifier in an iterative recognition process. We also present the development of a new structural signature adapted to noise context. The experimentation is realized on the noised symbols of GREC database [4]. Our experimentation presents a comparison with the two classical numerical classifiers that are the bayesian cl...

This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a ...

This paper is about the implementation of a visual tool for Differential Diagnosis of Erythemato-Squamous Diseases based on the classification algorithms; Nearest Neighbor Classifier (NN), Naive Bayesian Classifier using Normal Distribution (NBC) and Voting Feature Intervals-5 (VFI5). This tool enables the doctors to differentiate six types of ErythematoSquamous Diseases using clinical and hist...