Özet
In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of fundamental importance. Typically one is interested in determining which, of a large number of potentially redundant or noisy features, are most discriminative for classification. Searching all possible subsets of features is impractical computationally. This paper proposes a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative features. A fitness function is designed to determine the optimal number of features in a forward wrapper search. Bagging is applied to reduce the variance of the classifier and make a reliable selection. Experiments demonstrate that by selecting just 11 percent of the total features, the classifier can achieve better prediction on independent test data compared to the 70 percent of the total features selected by AdaBoost.
Orijinal dil | İngilizce |
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Ana bilgisayar yayını başlığı | Medical Imaging 2010 |
Ana bilgisayar yayını alt yazısı | Computer-Aided Diagnosis |
Editörler | Ronald M. Summers, Nico Karssemeijer |
Yayınlayan | SPIE |
ISBN (Elektronik) | 9780819480255 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2010 |
Harici olarak yayınlandı | Evet |
Etkinlik | Medical Imaging 2010: Computer-Aided Diagnosis - San Diego, United States Süre: 16 Şub 2010 → 18 Şub 2010 |
Yayın serisi
Adı | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
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Hacim | 7624 |
ISSN (Basılı) | 1605-7422 |
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???event.eventtypes.event.conference??? | Medical Imaging 2010: Computer-Aided Diagnosis |
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Ülke/Bölge | United States |
Şehir | San Diego |
Periyot | 16/02/10 → 18/02/10 |
Bibliyografik not
Publisher Copyright:© 2010 SPIE.