DocumentCode :
506934
Title :
Fuzzy Entropy of Classification and its Application to Biomarker Discovery
Author :
de Boves Harrington, P.
Author_Institution :
Clippinger Labs., OHIO Univ., Athens, OH, USA
Volume :
3
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
104
Lastpage :
108
Abstract :
Fuzzy entropy of classification is presented along with a criterion of maximizing the first derivative of the entropy with respect to temperature to optimize the degree of fuzziness. Fuzzy entropy of classification is used to construct classification trees comprised of multivariate fuzzy rules. These systems are of great use to scientists because of their discernable mechanism of inference. By using bootstrap Latin partitions statistically significant features can be ascertained from complex data sets. The principles are demonstrated with two simple simulated data sets.
Keywords :
chemical engineering; fuzzy set theory; inference mechanisms; pattern classification; statistical analysis; trees (mathematics); biomarker discovery; bootstrap Latin partitions; classification trees; fuzzy entropy; inference mechanism; multivariate fuzzy rules; Biomarkers; Classification tree analysis; Entropy; Fuzzy logic; Fuzzy systems; Hybrid intelligent systems; Instruments; Probability; Temperature; Toxic chemicals; Biomarker; Classification Tree; Fuzzy Entropy; Pattern Recognition; Proteomics Metablomics; Soft;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
Type :
conf
DOI :
10.1109/FSKD.2009.816
Filename :
5358907
Link To Document :
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