Abstract:

The Multiple Instance Choquet integral (MICI) for classifier fusion and an evolutionary algorithm for parameter estimation is presented. The Choquet integral has a long history of providing an effective framework for non-linear fusion. Previous methods to learn an appropriate measure for the Choquet integral assumed accurate and precise training labels (with low levels of noise). However, in many applications, data-point specific labels are unavailable and infeasible to obtain. The proposed MICI algorithm allows for training with uncertain labels in which class labels are provided for sets of data points (i.e., “bags”) instead of individual data points (i.e., “instances”). The proposed algorithm is able to fuse multiple two-class classifier outputs by learning a monotonic and normalized fuzzy measure from uncertain training labels using an evolutionary algorithm. It produces enhanced classification performance by computing Choquet integral with the learned fuzzy measure. Results on both simulated and real hyperspectral data are presented in the paper.

Links:

Citation:

X. Du, A. Zare, J. Keller, and D. Anderson, “Multiple Instance Choquet Integral for Classifier Fusion,” in IEEE Congress on Evolutionary Computation, Vancouver, BC, 2016, pp. 1054-1061.

@InProceedings{du2016multiple,
author = {Xiaoxiao Du and Alina Zare and James Keller and Derek Anderson},
title = {Multiple Instance Choquet Integral for Classifier Fusion},
booktitle = {IEEE Congress on Evolutionary Computation (CEC)},
year = {2016},
month = {July},
}