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increased agility that is more responsive to change, has lower IT cost to change, and achieves continuous optimization In statistics, the terms Type I error (a error, or false positive) and Type II error (b error, or a false negative) are used to describe possible errors made in a statistical decision process When an observer makes a Type I error in evaluating a sample against its parent population, he/she is mistakenly thinking that a statistical difference exists when in truth there is no statistical difference For example, imagine that a pregnancy test has produced a positive result (indicating that the woman taking the test is pregnant); if the woman is actually not pregnant, however, we say the test produced a false positive AType II error, or a false negative, is the error of failing to reject a null hypothesis when the alternative hypothesis is the true state of nature For example, a Type II error occurs if a pregnancy test reports negative when the woman is, in fact, pregnant Most classi cation systems for predicting bankruptcy have attempted to minimize the misclassi cations The minimizing misclassi cation approach assumes that Type I and Type II error costs of misclassi cation are equal This assumption may be a fallacy Remember that wisdom applied knowledge knowledge experience There is evidence that these costs are not equal and incorporating these costs into the classi cation systems can lead to superior classi cation systems The principles of evolution can be used to develop and test genetic algorithm (GA)-based neural and genetic programming (GP)-based classi cation approaches that incorporate the asymmetric Type I and Type II error costs By applying these arti cial intelligence methods and mechanisms to decision making, it is possible to move toward the smart data wisdom paradigm of knowledge experience, in which data comprise entities that also have attributes Using simulated and real-life bankruptcy data, we compared the results of the proposed approaches with statistical linear discriminant analysis (LDA), backpropagation arti cial neural network (ANN), and a GP-based classi cation approach that does not incorporate the asymmetric misclassi cation cost In essence, we are providing a data strategy map in order to make it easier to chart the course to higher performance We are oving toward wisdom because we are investigating both a linear and nonlinear approach and comparing the two outcomes The results indicate that the proposed approaches, incorporating Type I and Type II error cost asymmetries, result in lower Type I misclassi cations when compared to LDA, ANN, and GP approaches that do not incorporate misclassi cation costs These smart data truths should be self-evident and useful as a common thread among both government and private enterprise The smart data wisdom here is to investigate both the linear and nonlinear approaches and to compare the outcomes for the one that demonstrates the most cost containment We must remember that smart data is the product of engineering, such that it presents itself to the right users at the right place and time in support of planning, problem solving, decision making, sense making, and predicting In this case, there is.

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People and their organizations possess disciplines as arrayed in the Enterprise Performance Optimization Architecture as elements The disciplines are attributes assigned to one or more people This model says nothing about the number of individuals needed with these disciplines or about various combinations of disciplines residing among various people, as that is a function of enterprise size, scope, and complexity Determining what is best for your enterprise is a part of proprietary differentiation Furthermore, for each discipline type there are sources of best practices residing in academia and among technology vendors Terms We prepared this glossary to explain what we mean by these terms Many de nitions are from Wikipedia, Princeton, and the Department of Defense; some are our own unique offerings Performance Analysis (PA) This describes any analysis, spanning in scope from an enterprise view to process details and any combination in between Where information technologists might apply the term to investigating a program s behavior, we use the term more for understanding process performance, that is the sequence and timing of tasks and consumption of resources by enabling mechanisms to produce speci c outcomes Process modeling of the type we have used does not perform states and timing analysis, for instance There are a host of techniques supporting detailed analysis and understanding of processes and activity performance by combinations of people and technologies performing work Many techniques are embedded in drawing tools like Microsoft Visio and SmartDraw , for example More robust tools are needed for serious enterprise performance engineering and management Economic Analysis (EA) This form of analysis is also scalable to a range in scope One can use economic analysis to determine the cost effectiveness of a project or.

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