Fuzzy Entropy of Classification and its Application to Biomarker Discovery

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. Thes...

Full description

Saved in:
Bibliographic Details
Main Author: de Boves Harrington, Peter
Format: Conference Proceeding
Language:English
Subjects:
Biomarker
Biomarkers
Classification Tree
Classification tree analysis
Entropy
Fuzzy Entropy
Fuzzy logic
Fuzzy systems
Hybrid intelligent systems
Instruments
Pattern Recognition
Probability
Proteomics Metablomics
Soft
Temperature
Toxic chemicals
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
DOI:10.1109/FSKD.2009.816