Simple strategies for semi-supervised feature selection

Sechidis, Konstantinos; Brown, Gavin

doi:10.1007/s10994-017-5648-2

Cited by 38 publications

(18 citation statements)

References 38 publications

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“…For feature selection, one is interested in ranking the features in order of mutual information between the features and the label. Interestingly, this order remains the same when the unlabeled examples are considered as negative [89].…”

Section: Hypothesis Testingmentioning

confidence: 77%

Learning from positive and unlabeled data: a survey

2020

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Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

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Section: Hypothesis Testingmentioning

confidence: 77%

Learning from positive and unlabeled data: a survey

2020

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“…Semi -JMI is a method of using a semisupervised dataset as a training set for JMI. More details can be seen from Reference [36]. In this paper, the missingness mechanism is class-prior-change semisupervised scenario (MAR-C) [37].…”

Section: Methodsmentioning

confidence: 99%

Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

Guan

Zhao

Yang

2019

Computational Intelligence and Neuroscience

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This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.

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“…We expect that this tool will prove beneficial in visualizing and interpreting biomarker investigations for clinical trials. Finally, by formalizing the problem of predictive biomarker discovery in information theoretic terms, we can potentially extend this work to other challenging scenarios, such as misclassification bias ( Sechidis et al , 2017 ) or partially labelled data ( Sechidis and Brown, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Distinguishing prognostic and predictive biomarkers: an information theoretic approach

et al. 2018

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MotivationThe identification of biomarkers to support decision-making is central to personalized medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework.ResultsOur contribution is a novel procedure, INFO+, which naturally distinguishes the prognostic versus predictive role of each biomarker and handles higher order interactions. In a comprehensive empirical evaluation INFO+ outperforms more complex methods, most notably when noise factors dominate, and biomarkers are likely to be falsely identified as predictive, when in fact they are just strongly prognostic. Furthermore, we show that our methods can be 1–3 orders of magnitude faster than competitors, making it useful for biomarker discovery in ‘big data’ scenarios. Finally, we apply our methods to identify predictive biomarkers on two real clinical trials, and introduce a new graphical representation that provides greater insight into the prognostic and predictive strength of each biomarker.Availability and implementationR implementations of the suggested methods are available at https://github.com/sechidis.Supplementary information Supplementary data are available at Bioinformatics online.

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Simple strategies for semi-supervised feature selection

Cited by 38 publications

References 38 publications

Learning from positive and unlabeled data: a survey

Learning from positive and unlabeled data: a survey

Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

Distinguishing prognostic and predictive biomarkers: an information theoretic approach

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