Comprehensive Analysis of Hierarchical Aggregation Functions Decision Trees and Minimum Relative Entropy as Post Classifiers in the Classification of Fuzzy Based Epilepsy Risk Levels
R. HariKumar1, T.Vijayakumar2, M.G.Sreejith3

1Dr.R.Harikumar,Professor, ECE Department,.Bannari Amman Institute of Technology, Sathyamangalam India.
2T.Vijayakumar Assistant Professor,(Sr.Gr) Department of IT, Bannari Amman Institute of Technology, Sathyamangalam.
3M.G.Sreejith UG Student (ECE) Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 148-154 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1030102512/2012©BEIESP
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Abstract: 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).
Keywords: EEG Signals, Epilepsy Risk Levels, Fuzzy Logic, Hierarchical Decision Trees, Minimum Relative Entropy.