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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 ew 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 input 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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research is twofold: (1) we show how DEA can be used as a methodology to screen training cases, where forecasting models are subject to the managerial monotonicity assumption; and (2) we show how ANNs can be applied to forecast the number of employees in the healthcare industry Empirical studies are conducted to compare the predictive performance of ANNs ith learning from a set of DEA-based selected training cases, DEA-based rejected training cases, and a combined set of DEA-based selected and rejected training cases We use the publicly available data from healthcare facilities in Pennsylvania to learn about and predict the number of employees based on a set of predictor variables DEA was a technique introduced for comparing ef ciencies of decision making units (DMUs) The basic ratio DEA model seeks to determine a subset of k DMUs that determine the envelopment surface when all k DMUs consist of m inputs and s outputs The envelopment surface was determined by solving k linear programming models (one for each DMU), where all k DMUs appear in the constraints of the linear programming model ANNs have been applied to numerous nonparametric and nonlinear classi cation and forecasting problems In an ANN model, a neuron is an elemental processing unit that forms part of a larger network There are two basic types of ANNs: a single-layer (of connections) network and a double-layer (of connections) network A single-layer network, using the perceptron convergence procedure, represents a linear forecasting model A modi cation of the perceptron convergence procedure can be used to minimize the mean-square error between the actual and desired outputs in a two-layer network, which yields a nonlinear, multivariate forecasting model The backpropagation learning algorithm, most commonly used to train multilayer networks, implements a gradient search to minimize the squared error between realized and desired outputs Figure 33 shows a three-layer network that can be used for multivariate forecasting The number of input layer nodes corresponds to the number of independent variables describing the data The number of nodes in the hidden layer determines the complexity of the forecasting model and needs to be empirically determined to best suit the data being considered While larger networks tend to over t the data, too few hidden layer nodes can hinder learning of an adequate separation region Although having more than one hidden layer provides no advantage in terms of forecasting accuracy, it can in certain cases provide for faster learning The network is initialized with small random values for the weights, and the backpropagation learning procedure is used to update the weights as the data are iteratively presented to the input-layer neurons The weights are updated until some predetermined criterion is satis ed The number of hidden layer neurons is chosen as twice the number of data inputs, a commonly used heuristic in the literature Heuristics of the more the better could be used as a guide to select the number of hidden nodes in an ANN Furthermore, a network with a higher number of hidden nodes can always be considered as a special case of the network, with additional nodes (in the case of an ANN with a higher number of hidden nodes) having connection weights taking values equal to zero.

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