This article addresses a critical gap in existing research on granular computing by introducing a systematic and comprehensive method for handling double quantification in feature selection. This work significantly advances the understanding and application of rough approximation in multigranularity ordered decision systems. While previous studies have predominantly focused on single quantification, our research explores the simultaneous computing method of double quantification, which leads to enhanced accuracy and completeness in rough approximation. In addition, we derive a greedy algorithm specifically designed for feature selection within this framework. The significance of the article lies in its contribution to the field of granular computing and its practical implications for decision-making systems. This research has the potential to benefit various domains, including data mining, machine learning, and decision support systems. The findings highlight the advantages and effecti...

Double-quantitative-based granular computing implies the systematic perspective, completeness, and accuracy of rough approximation. However, most of the existing research works only focus on the case of single quantification, and there are few research study on the simultaneous computing method of double quantification. In this article, we explore feature selection with double quantification in multigranularity ordered decision systems (MG-ODSs). First, the related concepts of quantitative functions are interpreted from different viewpoints of relative and absolute quantification. Then, the multigranularity double-quantitative rough sets in an ordered decision system (ODS) from optimistic and pessimistic cases, the related properties, and three-way decisions based on the presented quantitative levels are discussed. Furthermore, the greedy algorithm for feature selection is derived. By using 12 datasets from a public repository, evaluations and comparisons are made on the parameter sett...