Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractAntifungal susceptibility and virulence profile of candida isolates from abnormal vaginal discharge of women from southern India    Next AbstractMeta-analysis of the role of entomopathogenic and unspecialized fungal endophytes as plant bodyguards »

IEEE/ACM Trans Comput Biol Bioinform


Title:Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification
Author(s):GaneshKumar P; Rani C; Devaraj D; Victoire TA;
Address:
Journal Title:IEEE/ACM Trans Comput Biol Bioinform
Year:2014
Volume:11
Issue:2
Page Number:347 - 360
DOI: 10.1109/TCBB.2014.2307325
ISSN/ISBN:1557-9964 (Electronic) 1545-5963 (Linking)
Abstract:"Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches"
Keywords:"*Algorithms Computational Biology/*methods Databases, Factual Diabetes Mellitus, Type 2/genetics/metabolism *Fuzzy Logic Gene Expression Profiling/*methods Humans Models, Biological Neoplasms/genetics/metabolism Oligonucleotide Array Sequence Analysis ROC;"
Notes:"MedlineGaneshKumar, Pugalendhi Rani, Chellasamy Devaraj, Durairaj Victoire, T Aruldoss Albert eng 2014/03/01 IEEE/ACM Trans Comput Biol Bioinform. 2014 Mar-Apr; 11(2):347-60. doi: 10.1109/TCBB.2014.2307325"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 26-06-2024