2019
DOI: 10.1109/access.2019.2915988
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Classification of ADHD Individuals and Neurotypicals Using Reliable RELIEF: A Resting-State Study

Abstract: Fractional amplitude of low-frequency fluctuation (fALFF) can reflect the intensity of spontaneous neuronal activity. Feature selection on functional magnetic resonance imaging (fMRI) data combined with fALFF can be well used to study the pathology of attention deficit hyperactivity disorder (ADHD) and assist in its diagnosis. However, the unsatisfactory effect of feature selection limits the study of ADHD. In this study, a novel method is proposed to classify ADHD individuals and neurotypicals. This work intr… Show more

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Cited by 37 publications
(19 citation statements)
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“…The parameter setting of our method, i.e., selected feature number N and subspace dimension K , is given in Table 2. Several state-of-the-art methods are employed for the comparison, including machine learning methods as R-Relief [11], L 1 BioSVM [27] and Fusion fMRI [22], deep learning methods as FCNet [29], 3D-CNN [31] and Deep fMRI [30], and two binary hypothesis methods with subspace learning and dual subspace learning respectively named as SP-BH [18] and Dual-SP-BH [19].…”
Section: Resultsmentioning
confidence: 99%
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“…The parameter setting of our method, i.e., selected feature number N and subspace dimension K , is given in Table 2. Several state-of-the-art methods are employed for the comparison, including machine learning methods as R-Relief [11], L 1 BioSVM [27] and Fusion fMRI [22], deep learning methods as FCNet [29], 3D-CNN [31] and Deep fMRI [30], and two binary hypothesis methods with subspace learning and dual subspace learning respectively named as SP-BH [18] and Dual-SP-BH [19].…”
Section: Resultsmentioning
confidence: 99%
“…Besides, more advanced strategies are in pursuit of better selection performance. For example, a Reliable Relief (R-Relief) method is recently presented and gives a set of feature weights to fractional ALFFs during feature selection [11]. As for FCs, a graph-kernel regularized LASSO is performed on the FC network, which preserves the local structure among the selected FCs [12].…”
Section: Introductionmentioning
confidence: 99%
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“…In order to diagnose this disorder automatically, MR images, including structural MRI (sMRI) and functional MRI (fMRI) have been investigated in many studies. The MRI data analysed in this paper were from the ADHD200 consortium [26,29]. Initially, they posted a large training dataset including 776 samples comprised of 491 typically developing individuals and 285 patients with ADHD.…”
Section: Attention Deficit Hyperactivity Disorder Diagnosismentioning
confidence: 99%