By Zbigniew Michalewicz
Within the glossy info period, managers needs to realize the aggressive possibilities represented via decision-support instruments. Adaptive enterprise intelligence structures mix prediction and optimization suggestions to help selection makers in advanced, quickly altering environments. those platforms tackle the elemental questions: what's prone to ensue sooner or later? and what's the simplest determination at once? Adaptive company Intelligence contains components of knowledge mining, predictive modeling, forecasting, optimization, and adaptability.The authors have massive educational learn backgrounds in synthetic intelligence and similar fields, mixed with years of functional consulting adventure in companies and industries world wide. during this ebook they clarify the technological know-how and alertness of diverse prediction and optimization suggestions, in addition to how those strategies can be utilized to enhance adaptive platforms. The strategies coated comprise linear regression, time-series forecasting, choice bushes and tables, synthetic neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant platforms, and agent-based modeling.This publication is acceptable for company and IT managers who make judgements in complicated business and repair environments, nonspecialists who are looking to comprehend the technological know-how at the back of greater predictions and judgements, and scholars and researchers who want a speedy creation to this box.
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Extra info for Adaptive Business Intelligence
If the adaptability module updates the prediction module every week by using a rolling time window, then the prediction module can adapt to changes in the sale prices. Alternatively, imagine that the prediction module has certain rules that can be expressed as follows: if [Make = Honda] and [Model = Accord] and [Color = white] and [40,000 < Mileage < 50,000] and [Year = 2000] and [Damage Level = $0], then Sale Price = $11,384. 5 The Structure of an Adaptive Business Intelligence System 45 Each of these rules has a weight,15 and the weights of rules can be modified (say, on a weekly basis) to tune the predictions in a changing environment.
3 Optimization 43 Let us also assume that our (very basic) prediction module “predicts” the sale price for each make/model at each auction site by looking at the average sale prices for the previous week (hence, it does not take into account the actual mileage, color, or other variables). It is interesting to note, however, that creating even a basic prediction module such as this presents many difficulties. For instance, a separate prediction model is needed for each make, model, and location.
New transactions) and the number of variables in each case (e. , the detail of each transaction). In general, the more data the better, as data mining can produce better results when performed on large data sets, and the resulting prediction models are more accurate. In the car distribution example, there are several important elements of prediction. For example, we would like to predict the sale prices for different cars at different auction sites on different days. Because these predictions are based on past cases, we should know all the variables (e.