Induction of Fuzzy Classification Systems Using Evolutionary ACO-Based Algorithms
Abadeh, Mohammad Saniee
Habibi, Jafar
Soroush, Emad
Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran;
This paper appears in: Modelling & Simulation, 2007. AMS '07. First Asia International Conference on
Publication Date: 27-30 March 2007
On page(s): 346-351
Location: Phuket,
ISBN: 0-7695-2845-7
INSPEC Accession Number: 9465144
Digital Object Identifier: 10.1109/AMS.2007.53
Current Version Published: 2007-04-10
Abstract
In this paper we have proposed an evolutionary algorithm to induct fuzzy classification rules. The algorithm uses an ant colony optimization based local searcher to improve the quality of final fuzzy classification system. The proposed algorithm is performed on intrusion detection as a high-dimensional classification problem. Results show that the implemented evolutionary ACO-Based algorithm is capable of producing a reliable fuzzy rule based classifier for intrusion detection
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