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We propose a supervised feature selection technique called the Optimal Loadings, that is based on applying the theory of Optimal Experiment Design (OED) to Partial Least Squares (PLS) regression. We apply the OED criterions to PLS with the goal of selecting an optimal feature subset that minimizes the variance of the regression model and hence minimize its prediction error. We show that the variance...
It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data sets. In this paper, we conduct a large empirical...
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