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Conformal Rule-Based Multi-label Classification
We advocate the use of conformal prediction (CP) to enhance rule-based m...
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On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
Recently, several authors have advocated the use of rule learning algori...
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Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning
Arguably the key reason for the success of deep neural networks is their...
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Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives
We analyze the RI-TIMEXes in temporally annotated corpora and propose tw...
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Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Being able to model correlations between labels is considered crucial in...
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Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm
Multi-label classification consists in classifying an instance into two ...
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SupRB: A Supervised Rule-based Learning System for Continuous Problems
We propose the SupRB learning system, a new Pittsburgh-style learning cl...
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Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.
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