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

Selecting a minimal feature set that is maximally informative about a targetvariable is a central task in machine learning and statistics. Informationtheory provides a powerful framework for formulating feature selectionalgorithms -- yet, a rigorous, information-theoretic definition of featurerelevancy, which accounts for feature interactions such as redundant andsynergistic contributions, is still missing. We argue that this lack isinherent to classical information theory which does not provide measures todecompose the information a set of variables provides about a target intounique, redundant, and synergistic contributions. Such a decomposition has beenintroduced only recently by the partial information decomposition (PID)framework. Using PID, we clarify why feature selection is a conceptuallydifficult problem when approached using information theory and provide a noveldefinition of feature relevancy and redundancy in PID terms. From thisdefinition, we show that the conditional mutual information (CMI) maximizesrelevancy while minimizing redundancy and propose an iterative, CMI-basedalgorithm for practical feature selection. We demonstrate the power of ourCMI-based algorithm in comparison to the unconditional mutual information onbenchmark examples and provide corresponding PID estimates to highlight how PIDallows to quantify information contribution of features and their interactionsin feature-selection problems.

A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition