Using an Adaptive Fuzzy Logic System to Optimise Knowledge Discovery in Proteomics

James Malone , Ken McGarry , Chris Bowerman
International Conference on Recent Advances in Soft Computing, pages 80--85, - Nov 2004
Associated documents :  
The growth of biomedical databases has seen a demand for data mining techniques to efficiently and effectively analyse the data contained within. One important consideration is the need to include expert’s opinions within the knowledge discovery process. However, this can be difficult to accomplish when such heuristics are presented in loosely defined, fuzzy terms. We present the use of an Adaptive Nero-Fuzzy Inference System (ANFIS) as an approach to optimizing such fuzzy opinions with the long term aim of incorporating such optimized rules into goal and data driven data mining of proteomic’s data.

 

@InProceedings{MMB04, 
 	 author =  {Malone, James and McGarry, Ken and Bowerman, Chris},  
 	 title = {Using an Adaptive Fuzzy Logic System to Optimise Knowledge Discovery in Proteomics}, 
 	 booktitle = {International Conference on Recent Advances in Soft Computing},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {80--85},
 	 year = {2004},
 	 month = {Nov},
 	 publisher = {Nottingham Trent University},
 	 doi = {}, 
 }