A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

This paper proposes a novel approach for the estimation of spectroscopic data by combining the predictions of an ensemble of estimators using the induced ordered weighted averaging (IOWA) fusion operators. For ensemble generation, we use Gaussian process regression (GPR) and extreme learning machine (ELM) estimators associated with different kernels. To render the model selection issue of ELM as efficiently as in the GPR Bayesian estimation method, we develop an automatic solution based on the powerful differential evolution (DE) algorithm. During the fusion process, the IOWA operator needs two things: (1) an order-inducing value; and (2) a way to determine its weights. For the order-inducing value, we propose to use the residual of each estimated output value. Because we cannot compute the true residual, we explore the idea of estimating the residuals themselves by associating to each estimator of the ensemble a second estimator of the same kind called a residual estimator. To learn the weights associated with these nonlinear operators, the proposed method relies on the concept of prioritized aggregation, where we generate the weights directly from the estimated residuals. Experimental results obtained on three real spectroscopic datasets confirm the interesting capabilities of the proposed IOWA fusion method.