Title :
Extracting information from fuzzy models
Author_Institution :
Int. Comput. Sci. Inst., Berkeley, CA, USA
Abstract :
The goal of this study is to quantify the importance of the input features in a fuzzy solution of two real-world problems. The first analysis definer the most effective ElectroCardioGraphic (ECG) measures for an automatic fuzzy recognition of cardiac arrhythmic beats. The second problem aims to assess the role played by duration, amplitude and pitch features of vocalic nuclei for the automatic recognition of prosodic stress in spoken American English. A similar analysis is performed for both problems by means of statistical decision trees under the form of the C4.5 algorithm. In general, decision trees and fuzzy systems seem to exploit the same or related input features for the analysis, especially if the input space dimension is low
Keywords :
decision trees; feature extraction; fuzzy systems; medical signal processing; speech recognition; automatic fuzzy recognition; cardiac arrhythmic beats; decision trees; fuzzy models; fuzzy systems; prosodic stress; spoken American English; statistical decision trees; vocalic nuclei; Algorithm design and analysis; Computer science; Data analysis; Data mining; Decision trees; Electrocardiography; Fuzzy systems; Performance analysis; Spatial databases; Stress;
Conference_Titel :
Fuzzy Information Processing Society, 2000. NAFIPS. 19th International Conference of the North American
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-6274-8
DOI :
10.1109/NAFIPS.2000.877380
