Fuzzy Rule Induction (FRI) is one of the main areas of research in the field of computational intelligence. Recently FRI has been successfully employed in the field of data mining in bioinformatics [34, 38]. Thanks to its flexibility and potentialities FRI allowed researchers to extract rules that can be easily modeled in natural language and submitted to experts in the field that can validate their accuracy or consistency. The process of FRI can result to be highly complex from a computational complexity point of view and, for this reason, several alternative approaches to accomplish this process have been proposed ranging from iterative and simultaneous algorithms[22] to Genetic Algorithms and Ant Colony Optimization based approaches[22]. In this chapter we will focus on a specific application of type-1 (T1) and type-2(T2) fuzzy systems to data mining in bioinformatics in which FRI is carried out using a novel and promising computational paradigm, namely Artificial Immune Systems (AIS). In order to provide the reader with the necessary theoretical background we will go through a brief introduction to the fields of AIS and T2 Fuzzy Systems, then we will set up the scientific context and describe applications of these concepts to real world cases. Conclusions and cues for future work in this fascinating field will be provided in the end.

Induction of Fuzzy Rules by Means of Artificial Immune Systems in Bioinformatics

Vitoantonio Bevilacqua;Giuseppe Mastronardi
2009

Abstract

Fuzzy Rule Induction (FRI) is one of the main areas of research in the field of computational intelligence. Recently FRI has been successfully employed in the field of data mining in bioinformatics [34, 38]. Thanks to its flexibility and potentialities FRI allowed researchers to extract rules that can be easily modeled in natural language and submitted to experts in the field that can validate their accuracy or consistency. The process of FRI can result to be highly complex from a computational complexity point of view and, for this reason, several alternative approaches to accomplish this process have been proposed ranging from iterative and simultaneous algorithms[22] to Genetic Algorithms and Ant Colony Optimization based approaches[22]. In this chapter we will focus on a specific application of type-1 (T1) and type-2(T2) fuzzy systems to data mining in bioinformatics in which FRI is carried out using a novel and promising computational paradigm, namely Artificial Immune Systems (AIS). In order to provide the reader with the necessary theoretical background we will go through a brief introduction to the fields of AIS and T2 Fuzzy Systems, then we will set up the scientific context and describe applications of these concepts to real world cases. Conclusions and cues for future work in this fascinating field will be provided in the end.
Fuzzy Systems in Bioinformatics and Computational Biology
978-3-540-89967-9
Springer
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/12835
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