Decision Support Systems, Год журнала: 2024, Номер 180, С. 114196 - 114196

Опубликована: Фев. 19, 2024

Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), starting point present work that different ways to categorize a given set objects exist, which depend choice sets features used classify them, such may yield better or worse categorizations, relative task at hand. In their turn, (a priori) particular over another might be subjective express certain epistemic stance (e.g. interests, relevance, preferences) an agent group agents, namely, interrogative agenda. paper, we represent agendas as features, explore compare w.r.t. (agendas). We first develop simple unsupervised FCA-based algorithm for outlier detection uses categorizations arising from agendas. then supervised meta-learning learn suitable (fuzzy) categorization with weights masses. combine this obtain algorithm. show these algorithms perform par commonly datasets detection. These provide both local global explanations results.

Язык: Английский