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

Authors:
Mu-Chun Su;Hsu-Hsun Lo;Fu-Hau Hsu
Affiliations:
Department of Computer Science & Information Engineering, National Central University, Jhongli 320, Taiwan, ROC;Department of Computer Science & Information Engineering, National Central University, Jhongli 320, Taiwan, ROC;Department of Computer Science & Information Engineering, National Central University, Jhongli 320, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 10
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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.