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Outlier detection using flexible categorisation and interrogative agendas
Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand.
A Meta-Learning Algorithm for Interrogative Agendas
These algorithms use a single concept lattice for such a task, meaning that the set of features used for the categorization is fixed.
Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory
The framework developed in this paper provides a formal ground to obtain and study explainable categorizations from the data represented as bipartite graphs according to the agendas of different agents in an organization (e. g.~an audit firm), and interaction between these through deliberation.
