A Hierarchical Feature and Sample Selection Framework and ItsApplication for Alzheimer's Disease Diagnosis

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473645.do
DOI
10.1038/srep45269
Title
A Hierarchical Feature and Sample Selection Framework and ItsApplication for Alzheimer's Disease Diagnosis
Description
This work was supported in part by NIH grants (EB006733, EB008374,MH100217, MH108914, AG041721, AG049371, AG042599, AG053867, EB022880,MH110274).
abstract
Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer's disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.
provenance
Made available in Cube on 2018-09-28T10:25:06Z (GMT). No. of bitstreams: 0
language
English
author
An, Le
Adeli, Ehsan
Liu, Mingxia
Zhang, Jun
Lee, Seong-Whan
Shen, Dinggang
accessioned
2018-09-28T10:25:06Z
available
2018-09-28T10:25:06Z
issued
2017
citation
SCIENTIFIC REPORTS(7)
issn
2045-2322
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473645.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345273936
Jurisdiction
Rep.of Korea
Project Name
Global Leader Development Division in Brain Engineering
rights
openAccess
type
article


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