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

Mu Chun Su, Hsu Hsun Lo, Fu Hau Hsu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish
Pages (from-to)7976-7985
Number of pages10
JournalExpert Systems with Applications
Volume37
Issue number12
DOIs
StatePublished - Dec 2010

Keywords

  • Decision tree
  • Incremental learning
  • Neural networks
  • Neural tree
  • Pattern classification
  • Spam detection

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