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- W4386002907 abstract "The useful balance of flexibility and robustness provided by fuzzy integrals for classification and regression tasks has been well established in the fuzzy research community. With the rise of artificial intelligence and neural networks, we have witnessed great advances in data analysis and decision-making, however the issue of explainability has become more prominent in recent times. It is in this regard that the theory of fuzzy integrals and other sophisticated aggregation frameworks have a lot to offer. In this contribution, a neural network architecture is proposed based on the inclusion-exclusion integral, which is defined with respect to a fuzzy measure and a triangular norm. After presenting the model and learning methodology, we provide some applications of the network to real datasets. The key benefit of this approach is in the ability to derive interpretations using the Shapley value and interaction indices, read directly from the model. However, we also highlight the predictive performance, which is comparable with state-of-the-art machine learning techniques. Our study hence contributes to the aims of explainable artificial intelligence, whereby model flexibility and accuracy is achieved without sacrificing interpretability." @default.
- W4386002907 created "2023-08-20" @default.
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- W4386002907 date "2023-11-01" @default.
- W4386002907 modified "2023-10-12" @default.
- W4386002907 title "A neural network based on the inclusion-exclusion integral and its application to data analysis" @default.
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- W4386002907 doi "https://doi.org/10.1016/j.ins.2023.119549" @default.
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