Adaptive and Natural Computing Algorithms: 10th by Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à

By Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth overseas convention on Adaptive and normal Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers provided have been conscientiously reviewed and chosen from a complete of a hundred and forty four submissions. the 1st quantity comprises forty two papers and a plenary lecture and is geared up in topical sections on neural networks and evolutionary computation.

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Additional resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I

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A brief summary of the data sets is given in Table 1, where we see that there are 5 domains with only discrete attributes, 9 with only continuous attributes and the remaining 6 have a mixture of both. Testing is performed using the leave-one-out cross-validation procedure. For each learning example that is left out in the current iteration, we compute the prediction and all the reliability estimates. The performance of reliability estimators is measured by computing the Spearman’s rank correlation coefficient between each set of reliability estimates and the real prediction error e(x) = 1 − fy (x).

Given the learning set L = [(x1 , c1 ), . . , (xl , cl )], the density estimate for unlabeled example (x, ) is therefore defined as p(x) = e∈L κ( (x, e) ) , l where κ denotes a kernel function (in our case Gaussian). Therefore the reliability estimate is given by: DEN S(x) = maxe∈L (p(e)) − p(x) . (5) 26 3 ˇ D. Pevec, E. Strumbelj, and I. Kononenko Results First, we describe the testing methodology, then we examine three main types of cases. We proceed and examine whether there are any performance differences between the estimators.

Exact forecasting of the energy consumption (fuel, gas, electrical energy) for each hour of the next day is an example of its application in engineering. The other example includes forecasting of the pollution for the next day, which enables to counteract its negative consequences for the health of the inhabitants. There are also many other important examples of time series predictions in our everyday life. The common practice allowing to obtain good accuracy of prediction is the application of many predictors working on the same input data and organized in the form of an ensemble [3],[11].

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