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Leveraging Constraints for User-Centric Selection of Predictive Features

Bach, J. ORCID iD icon 1
1 Institut für Programmstrukturen und Datenorganisation (IPD), Karlsruher Institut für Technologie (KIT)

Abstract:

Feature selection identifies the most valuable predictors in a dataset. Thus, feature-selection techniques are popular for obtaining small, interpretable, yet highly accurate prediction models. While optimizing technical quality metrics, standard feature-selection techniques might not satisfy user needs for two reasons. First, existing methods do not consider domain knowledge. Such domain knowledge can restrict which feature combinations make sense to users. Second, traditional feature-selection techniques typically yield only one feature set, which might not suffice in some scenarios. For example, users might be interested in finding different feature sets with similar prediction quality, offering alternative explanations of the data.
Constraints on feature sets alleviate both these shortcomings. First, constraints allow users to express domain knowledge, e.g., known physical laws, novel scientific hypotheses, etc. Second, constraints can formalize the notion of alternative feature sets.
Our research studies different types of constraints that make feature selection more user-centric. We investigate how to formulate and integrate such constraints into existing feature-selection techniques. ... mehr


Volltext §
DOI: 10.5445/IR/1000151285
Veröffentlicht am 10.10.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Poster
Publikationsdatum 06.10.2022
Sprache Englisch
Identifikator KITopen-ID: 1000151285
Veranstaltung AI Hub @ Karlsruhe (2022), Karlsruhe, Deutschland, 05.10.2022 – 07.10.2022
Schlagwörter feature selection, constraints, alternatives, explainability, interpretability
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