Learning Differential Diagnosis of Erythemato-Squamous Diseases using Voting Feature Intervals
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BibTeX
@MISC{Güvenir_learningdifferential,
author = {H. Altay Güvenir and Gülsen Demiröz and H. Altay Guvenir (corresponding and Gulsen Demiroz and Nilsel Ilter},
title = {Learning Differential Diagnosis of Erythemato-Squamous Diseases using Voting Feature Intervals},
year = {}
}
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Abstract
A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of Erythemato-Squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. Classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time. Keywords: Machine learning, differential diagnosis, erythemato-squamous, voting feature intervals 1 Introduction Researchers working on artificial intelligence have created many algorithms that successfully learn stra...
