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We are suggesting that there are better technical solutions that will afford greater agility and data exchangeability and which are less costly and produce harmonious performance The executive s speci cation for a smart data strategy must include this consideration Case Study: Technical Ef ciency Ranking Using the Data Envelopment Analysis The following case is a good example of how to improve optimization of human resources in a healthcare environment In this case, we show that when nonlinear machine learning models are used for learning monotonic forecasting functions, it may be necessary to screen training data so that the resulting examples satisfy the monotonicity property [11] We show how technical ef ciency ranking using the data envelopment analysis (DEA) model might be useful to screen training data so that a subset of examples that satisfy the monotonicity property can be identi ed Using real-life healthcare data, the managerial monotonicity assumption, and an arti cial neural network (ANN) as a forecasting model, we illustrate that DEA-based screening of training data improves forecasting accuracy of the ANN Earlier, we made the point that smart data is central to improving intelligent optimization as it leverages answers buried in information that is already available This case uses data mining, which is surely an aspect of smart data The case study answers many of the questions that top executives asked, such as, who are our customers, how much will they buy, when will they buy, what must we offer them, how much should we charge, what after-sales services should we offer, who are our competitors, and what must we do to provide a superior offering The answers to these questions provide the basis for prediction with a degree of certainty Every enterprise, including healthcare, has questions like these for which quantitative answers are readily determinable We have also pointed out that these questions accompany other more routine business questions such as how is our cash ow, what are the capital and material requirements, what are the enabling people and technology requirements, what are the processes involved, what are the performance metrics, what s the payback period and break-even, and what is the return on investment Observe that many of these questions are not answered in routine business accounting, although some are There is concern that application of some methods and algorithms and the resulting reports are not predictive They provide historical reporting from which analysis and comparison may serve as a retrospective report card, although not necessarily indicative about the future A smart data and smart data strategy approach improves ability of enterprises to prepare for and more accurately anticipate the future The art of management becomes more of a science in this regard whenever properties such as monotonicity are taken into consideration In certain forecasting problems it is very natural to assume that the forecasting function will satisfy the monotonicity property For example, in nancial forecasting models individual demand is shown to increase with one s income In medicine,.

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frontends for ANNs have been proposed These hybrid systems essentially automate the tedious work of preprocessing, including organizing data available from cases in a manner required by a speci c ANN model An expert system can also be used to select the best ANN paradigm for the data or to determine the optimal sample size of training and test sets Frameworks have been developed for expert system and hybrid technologies to assist in the improvement of ANN training Among the experimental questions posed were those targeting the need for guidelines in the selection of the training data Examining the example of character recognition, we see that an expert system is a natural t for training Genetic algorithms (GAs) are another area of arti cial intelligence (AI) application that can add value to the training and data selection tasks Developments in hybrid systems of this type show that most of the work performed in this area focuses on the advantages of employing GAs to search large spaces and nd an initial set of parameters for training an ANN A number of GA techniques can be used to select optimal values for data and parameters used in the ANN training process Bayesian approaches have recently been used to select the best ANN architecture in a set of several candidate architectures In particular, the Bayesian nonlinear regression model comparison procedure has been utilized to select an ANN architecture under which the training data set achieves the highest level of internal consistency Most of the techniques proposed in the literature attempt to increase the generalizability of ANN by removing inconsistent data or by adding random noise in the data A few researchers illustrate different procedures used to identify the inputs that may signi cantly (in a statistical sense) be related to the output variable While signi cant, none of the approaches focus on preserving the monotonicity property of the orecasting function learned by an ANN In our case, we propose DEA-based prescreening of ANN data to preserve the monotonicity property Our prescreening approach, while preserving monotonicity, doesn t hurt nonlinearity of the training data We view DEA as a complement to other approaches to training data selection When it is desired to increase generalizability of an ANN, DEA may not be an appropriate technique to use However, when it is desired to preserve the monotonicity property, DEA may be a suitable approach for prescreening training data DEA models provide an ef cient frontier, which is consistent with the monotonicity assumption In fact, all the ef cient DMUs (also called the reference set) lie on the ef cient frontier, which satis es the monotonicity property Figure 34 illustrates several frontiers for a single-input/single-output combination Frontier #1 is the ef cient frontier It can be shown that if DMUs lying on frontier #1 are taken out of the analysis, then frontier #2 becomes the next ef cient frontier and so on Assuming that all nine DMUs are used, it can be argued that DMUs lying on the ef cient frontier #1 have an ef ciency score equal to one and DMUs lying on frontier #2 have an ef ciency score of less than one, but the score is higher than the DMUs lying on frontier #3 ANNs use a least-squares error minimizing approach to learn nonlinear forecasting models If only DMUs lying on frontier #1 are used for training, then it can be argued.

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