Dealing with under-reported variables: An information theoretic solution

Sechidis, Konstantinos; Sperrin, Matthew; Petherick, Emily; Luj'n, Mikel; Brown, Gavin

doi:10.1016/j.ijar.2017.04.002

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…In surveys, under-reporting occurs when participants are likely to give false negative responses [91]. This occurs for issues that have social stigma, such as maternal smoking.…”

Section: Sources Of Pu Datamentioning

confidence: 99%

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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“…In surveys, under-reporting occurs when participants are likely to give false negative responses [91]. This occurs for issues that have social stigma, such as maternal smoking.…”

Section: Sources Of Pu Datamentioning

confidence: 99%

Learning from positive and unlabeled data: a survey

2020

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“…At this point we should emphasise that our novel estimators can be used with any information theoretic or machine learning procedure that requires the estimation of mutual or conditional MI. For example, we can use them to derive predictive biomarkers from clinical trial data , or to derive risk factors in underreported epidemiological data (Sechidis et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Efficient feature selection using shrinkage estimators

et al. 2019

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Information theoretic feature selection methods quantify the importance of each feature by estimating mutual information terms to capture: the relevancy, the redundancy and the complementarity. These terms are commonly estimated by maximum likelihood, while an under-explored area of research is how to use shrinkage methods instead. Our work suggests a novel shrinkage method for data-efficient estimation of information theoretic terms. The small sample behaviour makes it particularly suitable for estimation of discrete distributions with large number of categories (bins). Using our novel estimators we derive a framework for generating feature selection criteria that capture any high-order feature interaction for redundancy and complementarity. We perform a thorough empirical study across datasets from diverse sources and using various evaluation measures. Our first finding is that our shrinkage based methods achieve better results, while they keep the same computational cost as the simple maximum likelihood based methods. Furthermore, under our framework we derive efficient novel high-order criteria that outperform state-of-the-art methods in various tasks.

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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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Dealing with under-reported variables: An information theoretic solution

Cited by 19 publications

References 19 publications

Learning from positive and unlabeled data: a survey

Learning from positive and unlabeled data: a survey

Efficient feature selection using shrinkage estimators

Distinguishing prognostic and predictive biomarkers: an information theoretic approach

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