By Zbigniew Michalewicz
Within the sleek details period, managers needs to realize the aggressive possibilities represented through decision-support instruments. Adaptive company intelligence platforms mix prediction and optimization suggestions to aid determination makers in advanced, swiftly altering environments. those structures handle the elemental questions: what's more likely to occur sooner or later? and what's the simplest selection instantly? Adaptive company Intelligence comprises components of information mining, predictive modeling, forecasting, optimization, and adaptability.The authors have substantial educational study backgrounds in synthetic intelligence and similar fields, mixed with years of sensible consulting event in companies and industries around the globe. during this ebook they clarify the technological know-how and alertness of diverse prediction and optimization recommendations, in addition to how those suggestions can be utilized to enhance adaptive structures. The thoughts lined contain linear regression, time-series forecasting, selection timber and tables, synthetic neural networks, genetic programming, fuzzy platforms, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.This booklet is appropriate for company and IT managers who make judgements in advanced business and repair environments, nonspecialists who are looking to comprehend the technological know-how at the back of higher predictions and judgements, and scholars and researchers who desire a quickly creation to this box.
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Additional info for Adaptive Business Intelligence
2, which “learns” the average sale price for each make/model at each auction site by looking at the sale prices for the previous week. 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.
Despite the fact that prediction problems come in all shapes and sizes – varying in the number of variables, types of data patterns, time horizons, and types of expected output – only two types of prediction methods exist for addressing these problems: quantitative and qualitative methods. The quantitative methods assume that a sufficient amount of data exists about the past, that these data can be quantified in the form of numerical data, and that past patterns will continue into the future. Conversely, qualitative methods are applied in situations where very little quantitative data are available, but where sufficient qualitative knowledge exists.
As an example, imagine predicting what a particular Honda Accord (with known features and known mileage) would sell for at a particular auction site in Northern California at a particular point in time (e. , in two weeks). In this case, the input would be the auction site in Northern California, the 14 In many data mining activities, understanding what the columns mean and how they are related is not an easy task! 42 4 Adaptive Business Intelligence car Honda Accord (with all specified features), and the estimated sale date (say, May 21st, as today is May 7th).
Adaptive Business Intelligence by Zbigniew Michalewicz