EVOLVING RULES FROM NEURAL NETWORKS TRAINED ON BINARY AND CONTINUOUS DATA
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Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
NOVA SCIENCE PUBLISHERS, INC
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Although an Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from an ANN need to be formed to solve this problem and various methods have been improved to extract these rules. In this study, a new method that uses an Artificial Immune Systems (AIS) algorithm has been presented to extract rules from a trained ANN. The suggested algorithm does not depend on the ANN training algorithms; also, it does not modify the training results. This algorithm takes all input attributes into consideration and extracts rules from a trained neural network efficiently. This study demonstrates the use of AIS algorithms for extracting rules from trained neural networks. The approach consists of three phases: 1. data coding 2. classification of the coded data 3. rule extraction Continuous and noncontinuous values are used together in medical data. Regarding this, two methods are used for data coding and two methods (binary optimisation and real optimisation) are implemented for rule extraction. First, all data are coded binary and the optimal vectors are decoded and used to obtain rules. Then nominal data are coded binary and real data are normalized. After optimization, various intervals for continuous data are obtained and classification accuracy is increased.
Açıklama
Anahtar Kelimeler
artificial neural networks, artificial immune systems, optimization, rule extraction, backpropagation, Opt-aiNET
Kaynak
MACHINE LEARNING RESEARCH PROGRESS
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
