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Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Rybizki, Lydia - 2014
supervised learning. In addition, we investigate the effects of cost sensitive adaptations in CART and boosting and conclude that …
Persistent link: https://www.econbiz.de/10010334218
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Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Rybizki, Lydia - 2014
supervised learning. In addition, we investigate the effects of cost sensitive adaptations in CART and boosting and conclude that …
Persistent link: https://www.econbiz.de/10010250581
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AUK: a simple alternative to the AUC
Kaymak, Uzay; Ben-David, Ben-David, A.; Potharst, … - Erasmus Research Institute of Management (ERIM), … - 2010
The area under Receiver Operating Characteristic (ROC) curve, also known as the AUC-index, is commonly used for ranking … the performance of data mining models. The AUC has many merits, such as objectivity and ease of interpretation. However … proposed AUK-index compensates for the above basic flaw of the AUC by being sensitive to the class distribution. Therefore it …
Persistent link: https://www.econbiz.de/10010731206
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AUK: a simple alternative to the AUC
Kaymak, Uzay; Ben-David, A.; Potharst, R. - Erasmus Research Institute of Management (ERIM), ERIM … - 2010
The area under Receiver Operating Characteristic (ROC) curve, also known as the AUC-index, is commonly used for ranking … the performance of data mining models. The AUC has many merits, such as objectivity and ease of interpretation. However … proposed AUK-index compensates for the above basic flaw of the AUC by being sensitive to the class distribution. Therefore it …
Persistent link: https://www.econbiz.de/10008484125
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Big Data Analytics: A New Perspective
Chudik, Alexander; Kapetanios, George; Pesaran, M. Hashem - 2016
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised...
Persistent link: https://www.econbiz.de/10011451442
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Regularization in statistics
Bickel, Peter; Li, Bo; Tsybakov, Alexandre; Geer, Sara; … - In: TEST: An Official Journal of the Spanish Society of … 15 (2006) 2, pp. 271-344
Persistent link: https://www.econbiz.de/10005613248
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Big data analytics : a new perspective
Chudik, Alexander; Kapetanios, George; Pesaran, M. Hashem - 2016
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised...
Persistent link: https://www.econbiz.de/10011444508
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Big data analytics : a new perspective
Chudik, Akexander; Kapetanios, George; Pesaran, M. Hashem - 2016
Persistent link: https://www.econbiz.de/10011455779
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Noise Ratio As a Non-Nested Model Selection Tool.
Kurmanj, Agir - 2009
In this dissertation, we analyze whether the noise ratio statistic of Durlauf and Hall (1989), NRT, can be used as a non-nested model selection tool in a similar fashion to the Rivers and Vuong (2002) framework. For this purpose, we first show that, when scaled by the sample size T, NRT is...
Persistent link: https://www.econbiz.de/10009431155
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Model Selection for Solving Kinematics Problems
Goh, Choon P. - 1990
There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the...
Persistent link: https://www.econbiz.de/10009432937