Author:
Urszula Libal
Affiliation:
Wroclaw University of Technology, Poland
Keyword(s):
Multistage Classifier, Naive Bayes, Reject Option, Pattern Recognition, Wavelet, Resolution
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Bayesian Models
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
In the article, two approaches to pattern recognition of signals are compared: a direct and a multistage. It is assumed that there are two generic patterns of signals, i.e. a two-class problem is considered. The direct method classifies signal in one step. The multistage method uses a multiresolution representation of signal in wavelet bases, starting from a coarse resolution at the first stage to a more detailed resolutions at the next stages. After a signal is assigned to a class, the posterior probability for this class is counted and compared with a fixed level. If the probability is higher than this level, the algorithm stops. Otherwise the signal is rejected and on the next stage the classification procedure is repeated for a higher resolution of signal. The posterior probability is calculated again. The algorithm stops when the probability is higher than a fixed level and a signal is finally assigned to a class. The wavelet filtration of signal is used for feature selection an
d acts as a magnifier. If the posterior probability of recognition is low on some stage, the number of features on the next stage is increased by taking a better resolution. The experiments are performed for three local decision rules: naive Bayes, linear and quadratic discriminant analysis.
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