DocumentCode :
3133613
Title :
Feature Selection in Pathology Detection using Hybrid Multidimensional Analysis
Author :
Castellanos, G. ; Delgado, E. ; Daza, G. ; Sanchez, L.G. ; Suarez, J.F.
Author_Institution :
Control & Digital Signal Process. Group, Nat. Univ. of Colombia, NY
fYear :
2006
fDate :
Aug. 30 2006-Sept. 3 2006
Firstpage :
5503
Lastpage :
5506
Abstract :
Heuristical algorithms can reduce the computational complexity. Such methods require of some stopping criteria (cost function). Some of these cost functions are based on statistics like univariate and multivariate methods of analysis. Dimensional reduction techniques such as principal component analysis (PCA) allow to find a lower dimension transformed space based on data variance, but this procedure does not take into account information about classes separability, the direction of maximum variance does not necessarily correspond to the direction of maximum separability. In this work, we propose a feature selection algorithm with heuristic search that uses multivariate analysis of variance (MANOVA) as the cost function. This technique is put to test by classifying hypernasal from normal voices of CLP (cleft lip and/or palate) patients. The classification performance, computational time and reduction ratio are also considered by the comparison with an alternate feature selection method founded on unfolding the multivariate analysis into univariate and bivariate analysis
Keywords :
computational complexity; diseases; feature extraction; learning (artificial intelligence); medical computing; pattern classification; statistical analysis; bivariate analysis; cleft lip; computational complexity; cost function; feature selection algorithm; heuristical search algorithms; hybrid multidimensional analysis; multivariate analysis of variance; palate; pathology detection; pattern classification performance; training procedures; univariate analysis; Analysis of variance; Computational complexity; Cost function; Heuristic algorithms; Multidimensional systems; Pathology; Performance analysis; Principal component analysis; Statistical analysis; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
ISSN :
1557-170X
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2006.260740
Filename :
4463051
Link To Document :
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