By Zbigniew Michalewicz
Adaptive company intelligence platforms mix prediction and optimization innovations to help determination makers in advanced, speedily altering environments. those structures deal with primary questions: what's prone to take place sooner or later? what's the most sensible plan of action? Adaptive enterprise Intelligence explores components of information mining, predictive modeling, forecasting, optimization, and flexibility. The publication explains the appliance of diverse prediction and optimization strategies, and exhibits how those ideas can be utilized to boost adaptive structures. assurance comprises linear regression, time-series forecasting, selection bushes and tables, synthetic neural networks, genetic programming, fuzzy platforms, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.
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Extra info for Adaptive Business Intelligence
5). A classification problem may have, however, more than two outputs – in fact, the number of possible classes (i. , expected outputs) might be quite significant (e. , different types of diseases). In these classification problems, time does not exist; the “future” is understood as an arrival of a new (yet unknown) case, or it is included as a variable of the case. Similar comments are also applicable to regression problems. The general purpose of (multiple) regression is to discover the relationship between several independent (“predictor”) variables and a dependent (“criterion”) variable, with the output being a concrete number.
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).
Hence, we have to predict the sale price for each car a couple of weeks ahead of time, and take into account seasonality and other changes in the market. Again, let us illustrate this by an example. Say we are considering several possible auction sites for a car located in Jacksonville, Florida. Specifically, we are considering sending the car to an auction site in Georgia, Pennsylvania, or California. The price estimations for these three auction sites are different, because we are interested in an estimated sale price for 5 days from today for the Georgia auction site, an estimated sale price for 10 days from today for the Pennsylvania auction site, and an estimated sale price for 15 days from today for the California auction site.
Adaptive Business Intelligence by Zbigniew Michalewicz