Design of an Unsupervised Weight Parameter Estimation Method in Ensemble Learning

Authors:
Masato Uchida;Yousuke Maehara;Hiroyuki Shioya
Affiliations:
Network Design Research Center, Kyushu Institute of Technology, Fukuoka, Japan 801---0001;Graduate School of Computer Science and Systems Engineering, Muroran Institute of Technology, Hokkaido, Japan 050---8585;Department of Computer Science and Systems Engineering, Muroran Institute of Technology, Hokkaido, Japan 050---8585
Venue:
Neural Information Processing
Year:
2007

Citing 3
Cited 1

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Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995

Unsupervised Weight Parameter Estimation Method for Ensemble Learning

Journal of Mathematical Modelling and Algorithms

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Abstract

A learning method using an integration of multiple component predictors as an ultimate predictor is generically referred to as ensemble learning. The present paper proposes a weight parameter estimation method for ensemble learning under the constraint that we do not have any information of the desirable (true) output. The proposed method is naturally derived from a mathematical model of ensemble learning, which is based on an exponential mixture type probabilistic model and Kullback divergence. The proposed method provides a legitimate strategy for weight parameter estimation under the abovementioned constraint if it is assumed that the accuracy of all multiple predictors are the same. We verify the effectiveness of the proposed method through numerical experiments.