Extracting rules for classification problems: AIS based approach

Autor:	Humar Kahramanli, Novruz Allahverdi
Rok vydání:	2009
Předmět:	Hybrid neural network Artificial neural network Artificial Intelligence Computer science business.industry Artificial immune system General Engineering Artificial intelligence business Machine learning computer.software_genre computer Computer Science Applications
Zdroj:	Expert Systems with Applications. 36:10494-10502
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2009.01.029
Popis:	Although Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results in most cases may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In our previous work, a hybrid neural network was presented for classification (Kahramanli & Allahverdi, 2008). In this study a method that uses Artificial Immune Systems (AIS) algorithm has been presented to extract rules from trained hybrid neural network. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Cleveland heart disease and Hepatitis data. The proposed method achieved accuracy values 96.4% and 96.8% for Cleveland heart disease dataset and Hepatitis dataset respectively. It is been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bbd2fed8d40890d3e1dd7ae41c8b80b4 https://doi.org/10.1016/j.eswa.2009.01.029 Zobrazit plný text záznamu Full Text from ScienceDirect