The nature of statistical learning theory
The nature of statistical learning theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Symmetric curvature patterns for colonic polyp detection
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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Computed tomographic (CT) colonography is a promising alternative to traditional invasive colonoscopic methods used in the detection and removal of cancerous growths, or polyps in the colon. Existing computer-aided diagnosis (CAD) algorithms used in CT colonography typically employ the use of a classifier to discriminate between true and false positives generated by a polyp candidate detection system based on a set of features extracted from the candidates. However, these classifiers often suffer from a phenomenon termed the curse of dimensionality, whereby there is a marked degradation in the performance of a classifier as the number of features used in the classifier is increased. In addition, an increase in the number of features used also contributes to an increase in computational complexity and demands on storage space. This paper investigates the benefits of feature selection on a polyp candidate database, with the aim of increasing specificity while preserving sensitivity. Two new mutual information methods for feature selection are proposed in order to select a subset of features for optimum performance. Initial results show that the performance of the widely used support vector machine (SVM) classifier is indeed better with the use of a small set of features, with receiver operating characteristic curve (AUC) measures reaching 0.78-0.88.