Transfer Learning for the Choquet Integral

The Choquet integral (ChI) is a proven tool for information aggregation. In prior work, we showed that learning a ChI from data results in missing variables. Herein, we explore two ways to transfer a known ChI from a source domain to a new under sampled target domain. The first method is based on regularization and it listens to the full source domain ChI. The second method optimizes what we can observe (target domain supported variables) and missing variables are the only thing migrated from the source domain. Synthetic experiments, aka we know the truth, are used to show the behavior of these methods with respect to transfering between ChIs.

[1]  Ling Shao,et al.  Transfer Learning for Visual Categorization: A Survey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[2]  M. Grabisch.  The application of fuzzy integrals in multicriteria decision making , 1996 .

[3]  Timothy C. Havens,et al.  Data-Driven Compression and Efficient Learning of the Choquet Integral , 2018, IEEE Transactions on Fuzzy Systems.

[4]  Michel Grabisch,et al.  Classification by fuzzy integral: performance and tests , 1994, CVPR 1994.

[5]  Christophe Labreuche,et al.  Construction of a Choquet integral and the value functions without any commensurateness assumption in multi-criteria decision making , 2011, EUSFLAT Conf..

[6]  P. Gader,et al.  Advances in fuzzy integration for pattern recognition , 1994, CVPR 1994.

[7]  Derek T. Anderson,et al.  Fuzzy Choquet Integration of Deep Convolutional Neural Networks for Remote Sensing , 2018 .

[8]  James M. Keller,et al.  Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems , 2016, IPMU.

[9]  Timothy C. Havens,et al.  Visualization and learning of the Choquet integral with limited training data , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[10]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  Stacy E. Howington,et al.  Genetic programming based Choquet integral for multi-source fusion , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[12]  Paul D. Gader,et al.  Recognition technology for the detection of buried land mines , 2001, IEEE Trans. Fuzzy Syst..