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Found 7 papers, 0 papers with code
Precision and Recall Reject Curves for Classification
We therefore propose reject curves that evaluate precision and recall, the recall-reject curve and the precision-reject curve.
Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces
To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.
Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies
We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy.
Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory
We thus demonstrate the power of novel information-theoretic approaches in identifying relevant parameters in optimization runs and highlight how these methods avoid the selection of redundant parameters, while detecting interactions that result in synergistic contributions of multiple parameters.
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
We argue that this lack is inherent to classical information theory which does not provide measures to decompose the information a set of variables provides about a target into unique, redundant, and synergistic contributions.
Quantifying the predictability of visual scanpaths using active information storage
Entropy-based measures are an important tool for studying human gaze behavior under various conditions.
Computational Engineering, Finance, and Science Information Theory Information Theory Quantitative Methods
A Compact Spectral Descriptor for Shape Deformations
The proposed descriptor provides a novel approach to the parametrization of geometric deformation behavior and enables the use of state-of-the-art data analysis techniques such as machine learning to engineering tasks concerned with plastic deformation behavior.
