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Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2022 The Editor(s) (if applicable) and The Aut

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Imbalanced Dataset Optimization with New Resampling Techniques,the other hand, such algorithms are being extensively applied in many areas, showing promising results and outperforming other approaches. Therefore, many techniques have been developed to re-balance datasets in order to improve machine learning applicability. In this paper we present two algorithms
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Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data,so redefining the way banking is done. In this paper we evaluate the impact of graph-based techniques for credit risk assessment and how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, five networks are built and
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The Data Mining Dataset Characterization Ontology,ng knowledge bases have been developed, data mining assistants have also been proposed for algorithm selection. However, these studies focus on the description of algorithms, which makes a gap between the raw data and the ontology entities. This paper proposes a dataset characterization ontology, re
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Multi-Level Visualization with the MLV-Viewer Prototype,d by a computer in an interactive way, and in this way it allows supporting the decision maker and increasing his cognition. The appropriate tools, methods and techniques can increase the understanding of data, with greater importance if they were large in volume and multidimensional. These visual a
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Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data,tuted new forms of knowledge that enhance statistical and financial performance of credit risk models. Furthermore, opportunities are identified for Super-Apps to redefine the definition of credit risk by contemplating all the environment that their platforms entail, leading to a more inclusive financial system.
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