Visualization and knowledge discovery from interpretable models

Sreejita Ghosh*, Peter Tiño, Kerstin Bunte

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

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Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). So, in this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem, and visualisation of the classifier and decision boundaries: angle based variants of Learning Vector Quantization. The performance of the developed classifiers were comparable to those reported in literature for UCI’s heart disease dataset treated as a binary class problem. The newly developed classifiers also helped investigating the complexities of this dataset as a multiclass problem
Originele taal-2English
TitelInternational Joint Conference on Neural Networks (IJCNN)
Plaats van productieGlasgow, United Kingdom
UitgeverijIEEE (The Institute of Electrical and Electronics Engineers)
Aantal pagina's8
ISBN van geprinte versie978-1-7281-6926-2
StatusPublished - 1-jul.-2020
Evenement 2020 International Joint Conference on Neural Networks (IJCNN) - Glasgow, United Kingdom
Duur: 19-jul.-202024-jul.-2020


Conference 2020 International Joint Conference on Neural Networks (IJCNN)
Land/RegioUnited Kingdom

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