In this paper, we address ambiguity, intended as a characteristic of any data expression for which a unique meaning cannot be associated by the computational agent for either lack of information or multiple interpretations of the same configuration. In particular, we will propose and discuss ways in which a decision-support classifier can accept ambiguous data and make some (informative) value out of them for the decision maker. Towards this goal we propose a set of learning algorithms within what we call the three-way-in and three-way-out approach, that is, respectively, learning from partially labeled data and learning classifiers that can abstain. This approach is based on orthopartitions, as a common representation framework, and on three-way decisions and evidence theory, as tools to enable uncertain and approximate reasoning and inference. For both the above learning settings, we provide experimental results and comparisons with standard Machine Learning techniques, and show the advantages and promising performances of the proposed approaches on a collection of benchmarks, including a real-world medical dataset.

Campagner, A., Cabitza, F., Ciucci, D. (2020). The three-way-in and three-way-out framework to treat and exploit ambiguity in data. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 119, 292-312 [10.1016/j.ijar.2020.01.010].

The three-way-in and three-way-out framework to treat and exploit ambiguity in data

Campagner, Andrea
;
Cabitza, Federico
;
Ciucci, Davide
2020

Abstract

In this paper, we address ambiguity, intended as a characteristic of any data expression for which a unique meaning cannot be associated by the computational agent for either lack of information or multiple interpretations of the same configuration. In particular, we will propose and discuss ways in which a decision-support classifier can accept ambiguous data and make some (informative) value out of them for the decision maker. Towards this goal we propose a set of learning algorithms within what we call the three-way-in and three-way-out approach, that is, respectively, learning from partially labeled data and learning classifiers that can abstain. This approach is based on orthopartitions, as a common representation framework, and on three-way decisions and evidence theory, as tools to enable uncertain and approximate reasoning and inference. For both the above learning settings, we provide experimental results and comparisons with standard Machine Learning techniques, and show the advantages and promising performances of the proposed approaches on a collection of benchmarks, including a real-world medical dataset.
Articolo in rivista - Articolo scientifico
Three-way decision, Uncertainty, Ambiguity, Partial labels, Machine learning
English
292
312
21
print April 2020
Campagner, A., Cabitza, F., Ciucci, D. (2020). The three-way-in and three-way-out framework to treat and exploit ambiguity in data. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 119, 292-312 [10.1016/j.ijar.2020.01.010].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/259244
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