Comparison of decision tree classifiers with neural network and linear discriminant analysis classifiers for computer-aided diagnosis: a Monte Carlo simulation study

Berkman Sahiner; Lubomir M. Hadjiiski; Heang-Ping Chan; Chuan Zhou; Jun Wei

doi:10.1117/12.595761

29 April 2005 Comparison of decision tree classifiers with neural network and linear discriminant analysis classifiers for computer-aided diagnosis: a Monte Carlo simulation study

Berkman Sahiner, Lubomir M. Hadjiiski, Heang-Ping Chan, Chuan Zhou, Jun Wei

Author Affiliations +

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595761
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

The goal of this study was to compare the performance of decision tree (DT) classifiers with artificial neural network (ANN) and linear discriminant analysis (LDA) classifiers under different conditions for the class distributions, feature space dimensionality, and training sample size using a Monte Carlo simulation study. We also investigated a bagging technique for improving the accuracy of the DT. The resubstitution (training) and test accuracies of the classifiers were compared by using the area A_z under the ROC curve as the performance measure. Three types of feature space distributions were studied: the Gaussian feature space, a mixture of Gaussians, and a mixture of uniform distributions. The feature space dimensionality was varied between 2 and 12. In a given experiment, 1000 cases were randomly sampled from each distribution, N_t trainers per class was used for classifier design, and the remaining cases were used to test the classifier. The effect of the training sample size was investigated by varying N_t between 30 and 500. Performance measures from 100 experiments were averaged. Our results indicated that, in the Gaussian feature space, the LDA outperformed the other two classifiers, especially when the number of trainers was low. For the mixture of uniform distributions, the A_z value of the DT was in general higher than that of the ANN and the LDA. For the mixture of Gaussians, the performances of the DT and ANN classifiers were comparable. Our results indicate that a DT can be a viable alternative to ANN and LDA classifiers in certain feature spaces.

Citation Download Citation

Berkman Sahiner, Lubomir M. Hadjiiski, Heang-Ping Chan, Chuan Zhou, and Jun Wei "Comparison of decision tree classifiers with neural network and linear discriminant analysis classifiers for computer-aided diagnosis: a Monte Carlo simulation study", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595761

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