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|Title: ||Latent variable discovery in classification models|
|Authors: ||Zhang, Nevin Lianwen|
Nielsen, Thomas D.
Jensen, Finn V.
|Keywords: ||Naive Bayes model|
|Issue Date: ||2003 |
|Citation: ||To be publish in Artifical Intelligence in Medicine|
|Abstract: ||The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical applications, because it can lead to a better understanding of application domains. It can also improve classification accuracy and boost user confidence in classification models.|
|Rights: ||This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.|
|Appears in Collections:||CSE Preprints|
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