Description:

This Matlab package implements a method for learning a choquistic regression model (represented by a corresponding Moebius transform of the underlying fuzzy measure), using the maximum likelihood approach proposed in [2], eqquiped by sigmoid normalization, see [1]. For reasons of efficiency, it restricts to the 2-additive Choquet integral and implements the method, in which the Moebius transform is represented as a convex combination of a set of basis functions (for a detailed description of this method, see Section 4.2 in [3]).

[1] A. Fallah Tehrani, C. Labreuche, E. Huellermeier. Choquistic Utilitaristic Regression, DA2PL-2014

[2] A. Fallah Tehrani, W. Cheng, K. Dembczynski, E. Huellermeier. Learning Monotone Nonlinear Models using the Choquet Integral. Machine Learning, 89(1):183-211, 2012.

[3] E. Huellermeier, A. Fallah Tehrani. Efficient Learning of Classifiers based on the 2-additive Choquet Integral. In: C. Moewes and A. Nuernberger (eds.), Computational Intelligence in Intelligent Data Analysis, Studies in Computational Intelligence, Springer, pages 17-30, 2012.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Csv

Tags: Machine Learning, Data Mining

Archive: download here

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