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Methods of Classification for Credit Scoring based on Interval Pattern Structures

Student: Adam Khadzhimuradov

Supervisor: Sergei Kuznetsov

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Year of Graduation: 2021

This paper describes a classification algorithm based on interval pattern structures. The algorithm generates a set of positive and negative hypotheses for each object, and, based on the obtained hypotheses, predicts the class label. It uses a local sampling strategy and new hypotheses criterion. In addition, the possibility of using the hypotheses generated for each object as new features is considered.

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