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Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI (vol 11, 157, 2017)open access

Authors
Qureshi, Muhammad Naveed IqbalOh, JooyoungMin, BeomjunJo, Hang JoonLee, Boreom
Issue Date
Apr-2017
Publisher
FRONTIERS MEDIA SA
Keywords
ADHD-200; global functional connectivity; neuroimaging; ANOVA; machine learning; revised recursive feature elimination; hierarchical feature extraction; extreme learning machine
Citation
FRONTIERS IN HUMAN NEUROSCIENCE, v.11, pp.1 - 2
Indexed
SCIE
SCOPUS
Journal Title
FRONTIERS IN HUMAN NEUROSCIENCE
Volume
11
Start Page
1
End Page
2
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/152514
DOI
10.3389/fnhum.2017.00292
ISSN
1662-5161
Abstract
In the original article, there was a mistake in “TABLE 6 | Binary classification results” as published. We made errors while recording the supporting result values of sensitivity, specificity, F1-score, and precision. However, the main results of accuracy remain intact. To ensure the correctness and reproducibility of the results, we calculated all of these measures again. In addition, sensitivity, and recall represent the same measure, therefore, we omit the recall results. The corrected “TABLE 6 | Binary classification results” appears below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. © 2017 Qureshi, Oh, Min, Jo and Lee.
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