By Zbigniew Michalewicz
Within the smooth info period, managers needs to realize the aggressive possibilities represented by way of decision-support instruments. Adaptive enterprise intelligence structures mix prediction and optimization recommendations to help choice makers in advanced, speedily altering environments. those structures deal with the elemental questions: what's more likely to ensue sooner or later? and what's the simplest determination instantaneously? Adaptive company Intelligence comprises components of knowledge mining, predictive modeling, forecasting, optimization, and adaptability.The authors have substantial educational learn backgrounds in synthetic intelligence and similar fields, mixed with years of functional consulting event in companies and industries around the globe. during this booklet they clarify the technology and alertness of diverse prediction and optimization recommendations, in addition to how those strategies can be utilized to increase adaptive structures. The concepts lined contain linear regression, time-series forecasting, determination bushes and tables, man made neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.This e-book is acceptable for enterprise and IT managers who make judgements in advanced commercial and repair environments, nonspecialists who are looking to comprehend the technology in the back of greater predictions and judgements, and scholars and researchers who desire a speedy creation to this box.
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On the other hand, we could use an automatic method where the variable selection process is an inherent part of the prediction model. For example, when a decision tree is built (these are covered in the next section), the relevant variables are selected, one by one, during the treebuilding process. Lastly, we could also use automatic methods that evaluate the entire subset of variables. Many optimization techniques discussed in Chap. 6 would be appropriate for this type of approach, as the problem is really an optimization problem (i.
Another possibility would be to create some number of discrete categories (say, 20), and distribute all values to these categories in such a way that the average distance of a value from its category mean is the smallest. For example, the first category may contain mileages from 0 to 11,209, the second category may contain mileages from 11,789 to 18,991, and so on.
Hence, even if we possessed “perfect knowledge” and were capable of accurately predicting the price of any car at any auction site for any day, we still would not know how to optimally distribute 3,000 cars on any given day because of all the issues mentioned above. The number of possible distributions is simply too large to be processed in any reasonable amount of time. Hence, even though it is true that data mining can extract useful knowledge from data, it is a myth that the extraction of such knowledge will make a business run better!
Adaptive Business Intelligence by Zbigniew Michalewicz