Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data

Sechidis, Konstantinos; Brown, Gavin

doi:10.1007/978-3-319-23528-8_22

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Because the correction factor is a constant that depends on the amount of labeled data, one can calculate how much more data is required to get the desired power [90]. The conditional test of independence, which was used for learning the PTAN trees, has similar properties [9,88]. For feature selection, one is interested in ranking the features in order of mutual information between the features and the label.…”

Section: Hypothesis Testingmentioning

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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Section: Hypothesis Testingmentioning

confidence: 99%

Learning from positive and unlabeled data: a survey

2020

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“…This application of our work was first presented in Sechidis and Brown (2015). Firstly, we will show how we can use surrogate variables to derive the MB of positive-unlabelled nodes, a scenario where BASSUM cannot be applied.…”

Section: Application 1: Semi-supervised Markov Blanket Discoverymentioning

confidence: 99%

Simple strategies for semi-supervised feature selection

Sechidis

Brown

2017

Mach Learn

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What is the simplest thing you can do to solve a problem? In the context of semisupervised feature selection, we tackle exactly this-how much we can gain from two simple classifier-independent strategies. If we have some binary labelled data and some unlabelled, we could assume the unlabelled data are all positives, or assume them all negatives. These minimalist, seemingly naive, approaches have not previously been studied in depth. However, with theoretical and empirical studies, we show they provide powerful results for feature selection, via hypothesis testing and feature ranking. Combining them with some "soft" prior knowledge of the domain, we derive two novel algorithms (Semi-JMI, Semi-IAMB) that outperform significantly more complex competing methods, showing particularly good performance when the labels are missing-not-at-random. We conclude that simple approaches to this problem can work surprisingly well, and in many situations we can provably recover the exact feature selection dynamics, as if we had labelled the entire dataset.

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“…As we have illustrated in example (1), the output of an FS procedure is not always guaranteed to be of constant cardinality. Examples of such FS procedures are in feature selection by hypothesis testing [15]. For this reason, several attempts at extending this measure to feature sets of varying cardinality have been made in the literature, somehow losing some of the important properties.…”

Section: Quantifying Stabilitymentioning

confidence: 99%

Measuring the Stability of Feature Selection

Nogueira

Brown

2016

Lecture Notes in Computer Science

Self Cite

102

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Abstract. In feature selection algorithms, "stability" is the sensitivity of the chosen feature set to variations in the supplied training data. As such it can be seen as an analogous concept to the statistical variance of a predictor. However unlike variance, there is no unique definition of stability, with numerous proposed measures over 15 years of literature. In this paper, instead of defining a new measure, we start from an axiomatic point of view and identify what properties would be desirable. Somewhat surprisingly, we find that the simple Pearson's correlation coefficient has all necessary properties, yet has somehow been overlooked in favour of more complex alternatives. Finally, we illustrate how the use of this measure in practice can provide better interpretability and more confidence in the model selection process.

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Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data

Cited by 6 publications

References 12 publications

Learning from positive and unlabeled data: a survey

Learning from positive and unlabeled data: a survey

Simple strategies for semi-supervised feature selection

Measuring the Stability of Feature Selection

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