Corrigendum: Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI
- 1Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea
- 2Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- 3Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
by Qureshi, M. N. I., Oh, J., Min, B., Jo, H. J., and Lee, B. (2017). Front. Hum. Neurosci. 11:157. doi: 10.3389/fnhum.2017.00157
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.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Keywords: ADHD-200, global functional connectivity, neuroimaging, ANOVA, machine learning, revised recursive feature elimination, hierarchical feature extraction, extreme learning machine
Citation: Qureshi MNI, Oh J, Min B, Jo HJ and Lee B (2017) Corrigendum: Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI. Front. Hum. Neurosci. 11:292. doi: 10.3389/fnhum.2017.00292
Received: 11 May 2017; Accepted: 18 May 2017;
Published: 31 May 2017.
Edited and reviewed by: Peter Sörös, University of Oldenburg, Germany
Copyright © 2017 Qureshi, Oh, Min, Jo and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Boreom Lee, firstname.lastname@example.org