Cost-sensitive machine learning

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/att...

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Bibliographic Details
Other Authors: Krishnapuram, Balaji (-), Yu, Shipeng, Rao, Bharat
Format: eBook
Language:Inglés
Published: Boca Raton, Fla. : CRC Press c2012.
Boca Raton, Fla. : 2012.
Edition:1st edition
Series:Chapman & Hall/CRC machine learning & pattern recognition series.
Subjects:
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628717406719
Table of Contents:
  • pt. 1. Theoretical underpinnings of cost-sensitive machine learning
  • pt. 2. Cost-sensitive machine learning applications.