Feature selection and classification using flexible neural tree

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Chen:2006:N,

• author = "Yuehui Chen and Ajith Abraham and Bo Yang",
• title = "Feature selection and classification using flexible neural tree",
• journal = "Neurocomputing",
• year = "2006",
• volume = "70",
• number = "1-3",
• pages = "305--313",
• month = dec,
• note = "Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN '04), 7th Brazilian Symposium on Neural Networks",
• keywords = "genetic algorithms, genetic programming, Flexible neural tree model, Memetic algorithm, Intrusion detection system, Breast cancer classification",
• ISSN = "0925-2312",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.1041.7313",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1041.7313",
• URL = "http://www.softcomputing.net/neucom1.pdf",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231206001111",
• DOI = "doi:10.1016/j.neucom.2006.01.022",
• abstract = "The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimised by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate.",

}

Genetic Programming entries for Yuehui Chen Ajith Abraham Bo Yang

Citations