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Learning Monotone Nonlinear Models using the Choquet Integral

Learning Monotone Nonlinear Models using the Choquet Integral

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. The learning of predictive models that guarantee monotonicity in the input variables has received increasing attention in machine learning in recent years. While the incorporation of monotonicity constraints is rather simple for certain types of models, it may become a more intricate problem for others. By trend, the difficulty of ensuring monotonicity increases with the flexibility or, say, nonlinearity of a model. In this paper, we advocate the so-called Choquet integral as a tool for learning monotone nonlinear models. While being widely used as a flexible aggregation operator in different fields, such as multiple criteria decision... Show More


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