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Interpretation of linear classifiers by means of feature relevance bounds
Abstract Research on feature relevance and feature selection problems goes back several decades, but the importance of these areas continues to grow as more and more data becomes available, andExpand
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Feature Relevance Bounds for Linear Classification
Biomedical applications often aim for an identification of relevant features for a given classification task, since these carry the promise of semantic insight into the underlying process. ForExpand
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FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due toExpand
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Feature relevance bounds for ordinal regression
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, theExpand
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Bounds for Ordinal Regression Pfannschmidt
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, theExpand
Sequential Feature Classification in the Context of Redundancies
The problem of all-relevant feature selection is concerned with finding a relevant feature set with preserved redundancies. There exist several approximations to solve this problem but only one couldExpand
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
Abstract Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly interpretableExpand
University of Groningen Feature Relevance Bounds for Ordinal Regression Pfannschmidt
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, theExpand