English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The property of conformal predictors to guarantee the required accuracy ratemakes this framework attractive in various practical applications. However,this property is achieved at a price of reduction in precision. In the case ofconformal classification, the systems can output multiple class labels insteadof one. It is also known from the literature, that the choice of nonconformityfunction has a major impact on the efficiency of conformal classifiers.Recently, it was shown that different model-agnostic nonconformity functionsresult in conformal classifiers with different characteristics. For a NeuralNetwork-based conformal classifier, the inverse probability (or hinge loss)allows minimizing the average number of predicted labels, and margin results ina larger fraction of singleton predictions. In this work, we aim to furtherextend this study. We perform an experimental evaluation using 8 differentclassification algorithms and discuss when the previously observed relationshipholds or not. Additionally, we propose a successful method to combine theproperties of these two nonconformity functions. The experimental evaluation isdone using 11 real and 5 synthetic datasets.

How Nonconformity Functions and Difficulty of Datasets Impact the Efficiency of Conformal Classifiers