By Zbigniew Michalewicz
Adaptive company intelligence structures mix prediction and optimization thoughts to help determination makers in advanced, swiftly altering environments. those structures deal with basic questions: what's prone to occur sooner or later? what's the top plan of action? Adaptive enterprise Intelligence explores components of information mining, predictive modeling, forecasting, optimization, and flexibility. The ebook explains the applying of various prediction and optimization thoughts, and indicates how those strategies can be utilized to improve adaptive platforms. insurance contains linear regression, time-series forecasting, selection timber and tables, man made neural networks, genetic programming, fuzzy platforms, genetic algorithms, simulated annealing, tabu seek, ant platforms, and agent-based modeling.
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However, this does not mean that other components are not important (e. , an easy-to-use graphical user interface, a database for storing information). Thus, the overall structure of an Adaptive Business Intelligence system resembles the following diagram: 15 The “weight” of a rule is just a parameter that provides a relative measure of the rule’s merit. Rules that are more important are assigned higher values (recall the definition for parameter in Sect. 1). 46 4 Adaptive Business Intelligence In Sect.
Regardless of these complications, let us assume for the moment that our basic prediction module makes a prediction of $11,384 (for the Honda Accord being sent to an auction site in Northern California). Of course, this “prediction” would not be very accurate, but it serves as a good starting point for further discussion in Sect. 4 below. 3 Optimization Next comes the development of an optimization module capable of recommending the best answer. Note that the “best answer” is based on the prediction module’s output.
Because software development is covered many specialized texts, we will instead focus on the conceptual issues involved in developing an Adaptive Business Intelligence system. 2 Prediction 41 Since we have explained what each column means in the previous chapter,14 we should already have a good understanding of the data. Next, we have to clean the data. ” Yes, data can be “dirty”! This terminology refers to situations that involve missing data, inconsistent data, incorrect data, etc. Manually entered data usually has to be cleaned since humans make typing errors and often use different words to describe the same thing (e.
Adaptive Business Intelligence by Zbigniew Michalewicz