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Abstract: In corporate data mining applications, cost-sensitive learning is firmly established for predictive classification algorithms. Conversely, data mining methods for regression and time series analysis generally disregard economic utility and apply simple accuracy measures. Methods from statistics and computational intelligence alike minimise a symmetric statistical error, such as the sum of squared errors, to model ordinary least squares predictors. However, applications in business elucidate... (Update)
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BibTeX entry: (Update)
@misc{ for-utility,
author = "Cost-Sensitive Learning For",
title = "Utility based Data Mining for Time Series Analysis -",
url = "citeseer.ist.psu.edu/726457.html" }
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Documents on the same site (http://www.sven-crone.com):
Stepwise Selection of Artificial Neural Networks Models for Time.. - Crone (Correct)
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