کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
387586 660905 2009 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
چکیده انگلیسی

Extracting classification rules from data is an important task of data mining and gaining considerable more attention in recent years. In this paper, a new meta-heuristic algorithm which is called as TACO-miner is proposed for rule extraction from artificial neural networks (ANN). The proposed rule extraction algorithm actually works on the trained ANNs in order to discover the hidden knowledge which is available in the form of connection weights within ANN structure. The proposed algorithm is mainly based on a meta-heuristic which is known as touring ant colony optimization (TACO) and consists of two-step hierarchical structure. The proposed algorithm is experimentally evaluated on six binary and n-ary classification benchmark data sets. Results of the comparative study show that TACO-miner is able to discover accurate and concise classification rules.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 36, Issue 10, December 2009, Pages 12295–12305
نویسندگان
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