Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Automatic Quantification of MS Lesions in 3D MRI Brain Data Sets: Validation of INSECT
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this article we propose a framework for establishing individual structural networks. An individual network is established for each subject using the mean cortical thickness of cortical regions as defined by the AAL atlas. Specifically, for each subject, we compute a similarity matrix of mean cortical thickness between pairs of cortical regions, which we refer to hereafter as the individual's network. Such individual networks can be used for classification. We use a combination of two types of feature selection approaches to search for the most discriminative edges. These edges serve as the input to a support vector machine (SVM) for classification. We demonstrate the utility of the proposed method by a comparison with classifying the raw cortical thickness data, and individual networks, using a publically available dataset. In particular, 83 subjects from the OASIS database were chosen to validate this approach, 39 of which were diagnosed with either mild cognitive impairment (MCI) or moderate Alzheimer's disease (AD) and the remaining were age-matched controls. While using an SVM on the raw cortical thickness data or individual networks without hybrid feature selection resulted in less than or nearly 80% classification accuracy, our approach yielded 90.4% classification accuracy in leave-one-out analysis.