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Article
Application of Decision Trees for Mass Classification in Mammography
Bond Business School Publications
  • Kuldeep Kumar, Bond University
  • Ping Zhang
  • Brijesh Verma
Date of this Version
9-24-2006
Document Type
Conference Paper
Publication Details
Kumar, Kuldeep, Zhang, Ping and Verma, Brijesh (2006) Application of Decision Trees for Mass Classification in Mammography is a conference paper presented at The 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, September 2006, Xi'an, China.
A copy of this conference paper is published in the conference proceedings Advances in Natural Computation and Data Mining by Xidian University Press

2006 HERDC submission
Abstract

This paper discusses the effectiveness of using decision trees for mass classification in mammography. The decision tree algorithms implemented by CART (Classification and Regression Trees) and See5 were used for the experiments. Different costs for type I and type II misclassification were applied for the experiments. The results obtained using algorithms based on decision trees were compared with that produced by neural network which was reported giving the higher classification rate than statistical models, with higher standard deviation. It is concluded that the decision trees are very promising for the classification of breast masses in digital mammograms.

Citation Information
Kuldeep Kumar, Ping Zhang and Brijesh Verma. "Application of Decision Trees for Mass Classification in Mammography" (2006)
Available at: http://works.bepress.com/kuldeep_kumar/5/