Front. Hum. Neurosci.Frontiers in Human NeuroscienceFront. Hum. Neurosci.1662-5161Frontiers Media S.A.10.3389/fnhum.2017.00292NeuroscienceCorrectionCorrigendum: Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRIQureshiMuhammad Naveed Iqbal1OhJooyoung1MinBeomjun2JoHang Joon3LeeBoreom1*1Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and TechnologyGwangju, South Korea2Department of Neuropsychiatry, Seoul National University HospitalSeoul, South Korea3Department of Neurologic Surgery, Mayo ClinicRochester, MN, United States
Edited and reviewed by: Peter Sörös, University of Oldenburg, Germany
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A corrigendum on Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI 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.2017.00157ADHD-200global functional connectivityneuroimagingANOVAmachine learningrevised recursive feature eliminationhierarchical feature extractionextreme learning machine
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.
Binary classification results.
Classifier
Group
ADHDC-TDC
ADHDC-ADHDI
ADHDI-TDC
ELM
Accuracy (%)
89.286
85.714
92.857
p-value
<0.0001
<0.0001
<0.0001
Sensitivity
86.667
77.778
100.00
Specificity
92.310
100.00
87.500
F1-Score
89.655
87.500
92.307
Precision
92.857
100.00
85.714
ELM-NFS
Accuracy (%)
71.429
67.857
67.857
p-value
<0.0351
<0.0348
<0.0343
Sensitivity
100.00
77.780
69.231
Specificity
63.641
63.160
66.667
F1-Score
60.000
60.870
66.667
Precision
42.857
50.000
64.290
SVM linear
Accuracy (%)
71.429
82.143
67.857
Sensitivity
75.000
76.471
61.900
Specificity
68.750
90.910
85.714
F1-Score
69.231
83.869
74.290
Precision
64.286
92.857
92.857
SVM-RBF
Accuracy (%)
53.571
57.143
60.714
Sensitivity
53.333
55.556
66.667
Specificity
53.850
60.000
57.894
F1-Score
55.170
62.500
52.170
Precision
57.140
71.429
42.860
ELM, extreme learning machine; TDC, typically developing children; ADHDI, attention deficit/hyperactivity disorder-inattentive type; ADHDC, attention-deficit/hyperactivity disorder combined type; SVM, support vector machine; RBF, radial basis function; NFS, no feature selection applied. Besides the ELM-NFS all the three (ELM, SVM linear, and SVM-RBF) based classification scores were obtained with the most discriminative features selected through the hierarchical feature selection method. Bold values represents the highest accuracy and its corresponding evaluation measures.
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.