A New Approach to Classification by Means of Jumping Emerging Patterns

Aleksey Buzmakov (1) , Sergei O. Kuznetsov (2) , Amedeo Napoli (1)

1 ORPAILLEUR - Knowledge representation, reasonning
2 Department of Applied Mathematics - State University Higher School of Economics

Classification is one of the important fields in data analysis. Generating concept-based (JSM) hypotheses is a well-known approach to this task. Although the accuracy of this approach is quite good, the coverage is often insufficient. In this paper a new classification approach is presented. The approach is based on the similarity of an object to be classified to the current set of hypotheses: it attributes the new object to the class that minimizes the set of new hypotheses when a new object is added to the training set. The proposed approach provides a better coverage in comparison with the classical approach.

Mots clés: Classification; Formal Concept Analysis; JSM-Hypotheses; Jumping Emerging Patterns; Experiments

Domaines: Intelligence artificielle [cs.AI]

Aleksey Buzmakov, Sergei O. Kuznetsov, Amedeo Napoli. A New Approach to Classification by Means of Jumping Emerging Patterns. FCA4AI: International Workshop "What can FCA do for Artificial Intelligence?" - 2012, Dec 2012, Montpellier, France. ⟨hal-00761602⟩