Instance weighting through data imprecisiation

Lienen, Julian und Hüllermeier, Eyke (2021): Instance weighting through data imprecisiation. In: International Journal of Approximate Reasoning, Bd. 134: S. 1-14

Abstract

In machine learning, instance weighting is commonly used to control the influence of individual data points in a learning process. The general idea is to improve results (e.g., the accuracy of a predictor) by restricting the influence of training examples that do not appear to be representative and may bias the learner in an undesirable way. The simplest and most common approach is to modulate the influence of each data point through multiplicative scaling. In this paper, we elaborate on the idea of instance weighting through data imprecisiation as a viable alternative to existing methods, and formalize this approach within the framework of superset learning. Roughly speaking, the idea is to reduce the influence of training examples by turning a precise data point into an imprecise observation. Within the framework of optimistic superset learning, a generic approach to superset learning, this effectively comes down to modifying an underlying loss function on a per-instance basis. We illustrate our approach for the case of binary classification with support vector machines, showing that it compares favorably with existing approaches to instance weighting in support vector machines. In a further case study, we demonstrate the usefulness of instance weighting through data imprecisiation for the practical problem of depth estimation in monocular images. (C) 2021 Elsevier Inc. All rights reserved.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISSN: 0888-613X
• Sprache: Englisch