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 Iqbal; Oh, Jooyoung; Min, Beomjun; Jo, Hang Joon; Lee, 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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