Deep Forest in ADHD Data Classification
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Lizhen Shao | Dongmei Fu | Haipeng Du | Donghui Zhang | L. Shao | Dongmei Fu | Haipeng Du | Donghui Zhang
[1] B. Biswal,et al. Simultaneous assessment of flow and BOLD signals in resting‐state functional connectivity maps , 1997, NMR in biomedicine.
[2] Xin Zhang,et al. Abnormal Brain Activity in ADHD: A Study of Resting-State fMRI , 2017, BI.
[3] Eduardo Alonso,et al. Fusion of fMRI and non-imaging data for ADHD classification , 2017, Comput. Medical Imaging Graph..
[4] B. Biswal,et al. Cingulate-Precuneus Interactions: A New Locus of Dysfunction in Adult Attention-Deficit/Hyperactivity Disorder , 2008, Biological Psychiatry.
[5] Y. Zang,et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.
[6] Xiang-Yu Long,et al. Abnormal spontaneous brain activity in medication-naïve ADHD children: A resting state fMRI study , 2011, Neuroscience Letters.
[7] N. Tzourio-Mazoyer,et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.
[8] Yulin Zhang,et al. A feature selection method for classification of ADHD , 2017, 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS).
[9] Daoqiang Zhang,et al. Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA , 2016, Comput. Medical Imaging Graph..
[10] Muhammad Asad,et al. Deep fMRI: AN end-to-end deep network for classification of fMRI data , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[11] Yufeng Zang,et al. Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study , 2006, Neuroreport.
[12] Hang Joon Jo,et al. ADHD subgroup discrimination with global connectivity features using hierarchical extreme learning machine: Resting-state FMRI study , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[13] Dongmei Fu,et al. Classification of ADHD with bi-objective optimization , 2018, J. Biomed. Informatics.
[14] Karl J. Friston. Functional and Effective Connectivity: A Review , 2011, Brain Connect..
[15] Russell Greiner,et al. A general prediction model for the detection of ADHD and Autism using structural and functional MRI , 2018, PloS one.
[16] Ji Feng,et al. Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.
[17] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[18] Francisco Herrera,et al. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..
[19] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.
[20] Lianghua He,et al. Discrimination of ADHD Based on fMRI Data with Deep Belief Network , 2014, ICIC.
[21] Chunyan Miao,et al. 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI , 2017, IEEE Access.
[22] Yong He,et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.