Author/Authors
mağden, burak anadolu üniversitesi, ikieylül kampüsü - mühendislik fakültesi - bilgisayar mühendisliği bölümü, Turkey , telçeken, sedat anadolu üniversitesi, ikieylül kampüsü - mühendislik fakültesi - bilgisayar mühendisliği bölümü, Turkey
Title Of Article
CLASSIFICATION OF ECG SIGNALS BY APPROACHING PROBABILISTIC ROUGH SETS THEORY
شماره ركورد
34358
Abstract
In this study, surface pressure distributions on the two low-rise building models having different Classification in computer engineering is a process of grouping a data set with respect to its properties by computers rather than experts. Rough sets theory has been used as an effective tool for determining decision rules in classification problems in the recent years. In this study, ECG data of the cardiacs in Medical School at Eskisehir Osmangazi University is classified using rough sets theory (RST) and probabilistic rough sets theory (PRST). In order to evaluate the outcomes in the classification, accuracy and generality are used as evaluation criteria. In PRST, it is observed that these criteria are closely related with the threshold (α, β), which represents the conditional probability of an object being a member of a decision cluster or not. Depending on the average, 49% of improvement is observed in generality value of PRST.
From Page
159
NaturalLanguageKeyword
Rough sets theory , Probabilistic rough sets theory , ECG , Classification , Expert systems , Data mining
JournalTitle
Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering
To Page
166
JournalTitle
Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering
Link To Document