F-Ratio Test and Hypothesis Weighting : A Methodology to Optimize Feature Vector Size
Joint Authors
Source
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-08-17
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis.
This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension).
As a step towards such a statistical assessment, we present a data resampling approach.
The techniques allow estimating σ2(F), that is, the variance of an F-value from variance analysis.
Three test statistics are derived from the so-called F-ratio σ2(F)/F2.
A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting).
This leads to complete, partial, or noninclusion of these measures into an optimized feature vector.
We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic.
A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.
American Psychological Association (APA)
Dünki, R. M.& Dressel, M.. 2011. F-Ratio Test and Hypothesis Weighting : A Methodology to Optimize Feature Vector Size. Journal of Biophysics،Vol. 2011, no. 2011, pp.1-11.
https://search.emarefa.net/detail/BIM-460772
Modern Language Association (MLA)
Dünki, R. M.& Dressel, M.. F-Ratio Test and Hypothesis Weighting : A Methodology to Optimize Feature Vector Size. Journal of Biophysics No. 2011 (2011), pp.1-11.
https://search.emarefa.net/detail/BIM-460772
American Medical Association (AMA)
Dünki, R. M.& Dressel, M.. F-Ratio Test and Hypothesis Weighting : A Methodology to Optimize Feature Vector Size. Journal of Biophysics. 2011. Vol. 2011, no. 2011, pp.1-11.
https://search.emarefa.net/detail/BIM-460772
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-460772