Browsing by Subject "Naive Bayesian Classifier"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Classification by voting feature intervals
    (Springer, 1997-04) Demiröz, Gülşen; Güvenir, H. Altay
    A new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive Bayesian Classifier, which also considers each feature separately. Experiments on real-world datasets show that VFI achieves comparably and even better than NBC in terms of classification accuracy. Moreover, VFI is faster than NBC on all datasets. © Springer-Verlag Berlin Heidelberg 1997.
  • Thumbnail Image
    ItemOpen Access
    An expert system for the differential diagnosis of erythemato-squamous diseases
    (Elsevier, 2000) Güvenir, H. A.; Emeksiz, N.
    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 patient. The program also gives explanations for the classifications of each classifier. The patient records are also maintained in a database for further references. (C) 2000 Elsevier Science Ltd. All rights reserved.