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Proceedings Paper

Simultaneous feature selection and classification based on genetic algorithms: an application to colonic polyp detection
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Paper Abstract

Selecting a set of relevant features is a crucial step in the process of building robust classifiers. Searching all possible subsets of features is computationally impractical for large number of features. Generally, classifiers are used for the evaluation of the separability of a certain feature subset. The performance of these classifiers depends on some predefined parameters. However, the choice of these parameters for a given classifier is influenced by the given feature subset and vice versa. The computational cost for feature selection would be largely increased by including the selection of optimal parameters for the classifier (for each subset). This paper attempts to tackle the problem by introducing genetic algorithms (GAs) to combine the processes. The proposed approach can choose the most relevant features from a feature set whilst simultaneously optimising the parameters of the classifier. Its performance was tested on a colon polyp database from a cohort study using a weighted support vector machine (SVM) classifier. As a general approach, other classifiers such as artificial neural networks (ANN) and decision trees could be used. This approach could also be applied to other classification problems such as other computer aided detection/diagnosis applications.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150E (17 March 2008); doi: 10.1117/12.770561
Show Author Affiliations
Yalin Zheng, Medicsight PLC (United Kingdom)
Xiaoyun Yang, Medicsight PLC (United Kingdom)
Musib Siddique, Medicsight PLC (United Kingdom)
Gareth Beddoe, Medicsight PLC (United Kingdom)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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