A Two-Step Approach for Feature Selection and Classifier Ensemble Construction in Computer-Aided Diagnosis

Accurate classification methods are critical in computer-aided diagnosis and other clinical decision support systems. Previous research has studied methods for combining genetic algorithms for feature selection with ensemble classifier systems in an effort to increase classification accuracy. We pro...

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Bibliographic Details
Main Authors: Lee, M.C., Boroczky, L., Sungur-Stasik, K., Cann, A.D., Borczuk, A.C., Kawut, S.M., Powell, C.A.
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Cancer
classifier ensemble
Computed tomography
Computer aided diagnosis
feature selection
genetic algorithm
Genetic algorithms
lung cancer
Lungs
Machine learning
Performance evaluation
USA Councils
Voting
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Summary:Accurate classification methods are critical in computer-aided diagnosis and other clinical decision support systems. Previous research has studied methods for combining genetic algorithms for feature selection with ensemble classifier systems in an effort to increase classification accuracy. We propose a two-step approach that first uses genetic algorithms to reduce the number of features used to characterize the data, then applies the random subspace method on the remaining features to create a set of diverse but high performing classifiers. These classifiers are combined using ensemble learning techniques to yield a final classification. We demonstrate this approach for computer-aided diagnosis of solitary pulmonary nodules from CT scans, in which the proposed method outperforms several previously described methods.
ISSN:1063-7125
DOI:10.1109/CBMS.2008.68