标题：Comprehensive Analysis of Hierarchical Aggregation Functions Decision Trees and Minimum Relative Entropy as Post Classifiers in the Classification of Fuzzy Based Epilepsy Risk Levels
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：5
页码：148-154
出版社：International Journal of Soft Computing & Engineering
摘要：The objective of this paper is to compare the performance of Hierarchical Soft (max-min) decision trees and Minimum Relative Entropy (MRE) in optimization of fuzzy outputs in the classification of epilepsy risk levels from EEG (Electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Hierarchical Soft decision tree and Minimum Relative Entropy (post classifiers with max-min criteria) four types are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI), and Quality Value (QV).
