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

Mu Chun Su, Hsu Hsun Lo, Fu Hau Hsu

研究成果: 雜誌貢獻期刊論文同行評審

15 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)7976-7985
頁數10
期刊Expert Systems with Applications
37
發行號12
DOIs
出版狀態已出版 - 12月 2010

指紋

深入研究「A neural tree and its application to spam e-mail detection」主題。共同形成了獨特的指紋。

引用此