A literature review on one-class classification and its potential applications in big data
N Seliya, A Abdollah Zadeh, TM Khoshgoftaar - Journal of Big Data, 2021 - Springer
In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …
leads to bias towards the class (es) with the much larger number of instances. Under such …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Machine learning in major depression: From classification to treatment outcome prediction
S Gao, VD Calhoun, J Sui - CNS neuroscience & therapeutics, 2018 - Wiley Online Library
Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …
morbidity, and affects about 10% of the population worldwide. Currently, there are no …
[HTML][HTML] Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies
Background Despite many successes, the case-control approach is problematic in
biomedical science. It introduces an artificial symmetry whereby all clinical groups (eg …
biomedical science. It introduces an artificial symmetry whereby all clinical groups (eg …
Towards a brain‐based predictome of mental illness
Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …
recent years and have deepened our understanding of both cognitively healthy and …
PRoNTo: pattern recognition for neuroimaging toolbox
In the past years, mass univariate statistical analyses of neuroimaging data have been
complemented by the use of multivariate pattern analyses, especially based on machine …
complemented by the use of multivariate pattern analyses, especially based on machine …
Making individual prognoses in psychiatry using neuroimaging and machine learning
Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the
future, as opposed to making a diagnosis, which is concerned with the current state. During …
future, as opposed to making a diagnosis, which is concerned with the current state. During …
[HTML][HTML] Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels,
including symptoms, disease course, and biological underpinnings. These form a …
including symptoms, disease course, and biological underpinnings. These form a …
[HTML][HTML] Diagnostic neuroimaging across diseases
S Klöppel, A Abdulkadir, CR Jack Jr, N Koutsouleris… - Neuroimage, 2012 - Elsevier
Fully automated classification algorithms have been successfully applied to diagnose a wide
range of neurological and psychiatric diseases. They are sufficiently robust to handle data …
range of neurological and psychiatric diseases. They are sufficiently robust to handle data …
Hybrid approach to document anomaly detection: an application to facilitate RPA in title insurance
Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is
no exception. Robotic process automation (RPA) is taking over manual tasks in TI business …
no exception. Robotic process automation (RPA) is taking over manual tasks in TI business …