GENETIC ALGORITHM OPTIMIZED FEATURE EXTRACTION AND SELECTION FOR ECG PATTERN CLASSIFICATION

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" GENETIC ALGORITHM OPTIMIZED FEATURE EXTRACTION AND SELECTION FOR ECG PATTERN CLASSIFICATION "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	150501
Doc. No	:	ET22293
Main Entry	:	huang
Title Proper	:	GENETIC ALGORITHM OPTIMIZED FEATURE EXTRACTION AND SELECTION FOR ECG PATTERN CLASSIFICATION
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	The Genetic Algorithm (GA) is used for the classification problem of Electrocardiogram(ECG), by optimizing the weights in feature extraction and selection. Most of the relatedapproaches in GA optimized feature classification and selection focus on some particularclassifier settings and the results are mostly empirical. In this paper, the featureweighting of three simple classifiers: The k-Nearest Neighbor classifier, the BayesClassifier and the Linear Regression Classifier are tested to give a more comprehensiveevaluation of GA. The utility of the feature optimization on these classifiers is analyzedanalytically and is tested empirically. Some traditional-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL45499.pdf
Title and statement of responsibility and	:	GENETIC ALGORITHM OPTIMIZED FEATURE EXTRACTION AND SELECTION FOR ECG PATTERN CLASSIFICATION [Thesis]