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a patient toleration level for a drug increases with effectiveness of treatment A few researchers in information systems have shown that a rm s nancial performance increases with an increase in its information technology (IT) investment Several models in economics, operations research, and transportation science are required to satisfy the monotonicity property The monotonicity property of the forecasting function makes it easy to aggregate forecasts For example, assuming monotonicity of individual preferences relates individual preferences to predict market behavior and preserving monotonicity reduces over tting problem in a nonlinear algorithm For a linear forecasting function, it is easy to check if the function satis es the monotonicity property Perhaps some measures such as positive values of regression coef cients can be used to ensure that the learned forecasting function satis es the monotonicity property When it is desired to use a nonlinear forecasting function, monotonicity can be preserved by (1) redesigning the learning algorithm so that the resulting forecasting function learned by the nonlinear algorithm preserves monotonicity, or (2) using the training data set that does not violate the monotonicity property There are several nonlinear machine learning forecasting algorithms available The examples of nonlinear machine learning algorithms include arti cial neural network (ANN), classi cation and regression tree (CART), and genetic programming (GP) All of these algorithms do not assume any particular functional form of the forecasting function and learn the nonlinear forecasting function based solely on the properties of the training data set Since the functional form of the machine learning algorithm depends on the training data, it may be necessary that the training data satisfy the monotonicity property When training data violate the monotonicity property, some data screening may be required to create a training data subsample that does not violate monotonicity In a small-size data set, such data screening may not be feasible due to very small resulting subsample size However, in mid- to large-size data sets, it may be possible to create a subsample that preserves monotonicity In mid-size data sets, if a subsample that preserves monotonicity is too small, then perhaps a large subsample, not containing all the original training data, may be created with the subsample data that weakly preserves monotonicity The term weak monotonicity means that most of the cases in the training subsample satisfy the monotonicity property, and a few cases may somewhat violate monotonicity The design of the data screening algorithm that may be used to create a subsample is an important issue It is important that the screening algorithm should be nonlinear, preserve monotonicity, and not assume any particular nonlinear functional form For multivariate nput and one output, the data envelopment analysis (DEA) model seems to satisfy all the requirements The DEA model is a nonlinear (piecewise linear) and nonparametric model that is used to measure ef ciency of production units In this case, we use DEA as a data screening approach to create a subsample training data set that is weakly monotonic We use the ANN as a nonlinear forecasting model The contribution of our.

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