Learning Monotone Nonlinear Models using the Choquet Integral

author: Weiwei Cheng, Mathematik und Informatik, Philipps-Universität Marburg
published: Oct. 3, 2011,   recorded: September 2011,   views: 248


Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography


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 making, the Choquet integral is much less known in machine learning so far. Apart from combining monotonicity and flexibility in a mathematically sound and elegant manner, the Choquet integral has additional features making it attractive from a machine learning point of view. Notably, it offers measures for quantifying the importance of individual predictor variables and the interaction between groups of variables. As a concrete application of the Choquet integral, we propose a generalization of logistic regression. The basic idea of our approach, referred to as choquistic regression, is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive class, by the Choquet integral.

See Also:

Download slides icon Download slides: ecmlpkdd2011_cheng_learning_01.pdf (1020.8¬†KB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: