A neural tree and its application to spam e-mail detection

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/51511

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51511

Title:	A neural tree and its application to spam e-mail detection
Authors:	Su,MC;Lo,HH;Hsu,FH
Contributors:	資訊工程學系
Keywords:	NETWORKS;CLASSIFICATION;EXTRACTION
Date:	2010
Issue Date:	2012-03-27 18:54:50 (UTC+8)
Publisher:	國立中央大學
Abstract:	This paper presents a new approach to constructing a neural tree to integrate the advantages of decision trees and neural networks. The proposed neural tree, called a quadratic-neuron-based neural tree (QUANT), is a tree-structured neural network composed of neurons with quadratic neural-type junctions for pattern classification. A quadratic neuron is capable of forming a hyper-ellipsoid that can be varied in sizes and in locations on the space spanned by the input variables. Via a batch-mode training algorithm, the QUANT grows a neural tree containing quadratic neurons in its nodes. These quadratic neurons recursively partition the feature space into hyper-ellipsoidal-shaped sub-regions. The QUANT has the partial incremental capability so that it does not need to re-construct a new neural tree to accommodate new training data whenever new data are introduced to a trained QUANT. To demonstrate the performance of the proposed QUANT, one pattern recognition problem and the spam e-mail detection problem were tested. (C) 2010 Elsevier Ltd. All rights reserved.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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