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Durham Research Online
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Binary credal classification under sparsity constraints.

Basu, Tathagata and Troffaes, Matthias C. M. and Einbeck, Jochen (2020) 'Binary credal classification under sparsity constraints.', in Information processing and management of uncertainty in knowledge-based systems : 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, proceedings, Part II. , pp. 82-95.

Abstract

Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle sparse data. However, a convincing approach to the classification problem in high dimensional problems (i.e., when the number of attributes is larger than the number of observations) is yet to be explored in the context of imprecise probability. In this article, we propose a sensitivity analysis based on penalised logistic regression scheme that works as binary classifier for high dimensional cases. We use an approach based on a set of likelihood functions (i.e. an imprecise likelihood, if you like), that assigns a set of weights to the attributes, to ensure a robust selection of the important attributes, whilst training the model at the same time, all in one fell swoop. We do a sensitivity analysis on the weights of the penalty term resulting in a set of sparse constraints which helps to identify imprecision in the dataset.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 05 June 2021.
(AM) Accepted Manuscript
File format - PDF
(353Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-030-50143-3_7
Publisher statement:This is a post-peer-review, pre-copyedit version of an book chapter published in Information processing and management of uncertainty in knowledge-based systems : 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, proceedings, Part II. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-50143-3_7
Date accepted:18 March 2020
Date deposited:09 June 2020
Date of first online publication:05 June 2020
Date first made open access:05 June 2021

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