Impact Statement:Capturing information as intervals provides a powerful means for handling uncertainty and inherent range in data. This paper focuses on a basic building block of statisti...Show More
Abstract:
Most of statistics and AI draw insights through modeling discord or variance between sources (i.e., intersource) of information. Increasingly however, research is focusin...Show MoreMetadata
Impact Statement:
Capturing information as intervals provides a powerful means for handling uncertainty and inherent range in data. This paper focuses on a basic building block of statistics and AI: linear regression. In recent years, regression for intervals has become a topic of interest in AI and has been applied to domains ranging from marketing to cyber-security. It allows direct modelling of relationships not only between variables per-se, but also their associated uncertainty. For example, we can infer not only how a snack's nutritional benefits impact consumer purchase intention, but also how uncertainty about these benefits impacts purchase intention and its associated uncertainty. Nonetheless, while there are considerable upsides to interval regression and AI, substantial challenges remain. This paper reviews and extends state-of-the-art interval regression methods, presents in-depth experimental results and introduces a novel visualization approach for interval regression—enhances interpretab...
Abstract:
Most of statistics and AI draw insights through modeling discord or variance between sources (i.e., intersource) of information. Increasingly however, research is focusing on uncertainty arising at the level of individual measurements (i.e., within- or intrasource), such as for a given sensor output or human response. Here, adopting intervals rather than numbers as the fundamental data-type provides an efficient, powerful, yet challenging way forward—offering systematic capture of uncertainty-at-source, increasing informational capacity, and ultimately potential for additional insight. Following progress in the capture of interval-valued data in particular from human participants, conducting machine learning directly upon intervals is a crucial next step. This article focuses on linear regression for interval-valued data as a recent growth area, providing an essential foundation for broader use of intervals in AI. We conduct an in-depth analysis of state-of-the-art methods, elucidating...
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 5, Issue: 1, January 2024)