Flexible categorization using formal concept analysis and Dempster-Shafer theory
The framework developed in the present paper provides a formal ground to generate and study explainable categorizations of sets of entities, based on the epistemic attitudes of individual agents or groups thereof. Based on this framework, we discuss a machine-leaning meta-algorithm for outlier detection and classification which provides local and global explanations of its results.
Code (0)
등록된 구현이 없습니다.
Tasks
Outlier DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory
Categorization of business processes is an important part of auditing. Large amounts of transnational data in auditing can be represented as transactions between financial accounts using weighted bipartite graphs. We vie…
Toward a Dempster-Shafer theory of concepts
In this paper, we generalize the basic notions and results of Dempster-Shafer theory from predicates to formal concepts. Results include the representation of conceptual belief functions as inner measures of suitable pro…
Outlier detection using flexible categorisation and interrogative agendas
Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of object…
Meta-LearningOutlier DetectionA Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition
We formulate Dempster Shafer Belief functions in terms of Propositional Logic using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of propositional clauses, assigning weights to certain…
Soft Concept Analysis
In this chapter we discuss soft concept analysis, a study which identifies an enriched notion of "conceptual scale" as developed in formal concept analysis with an enriched notion of "linguistic variable" as discussed in…
Philosophy