Open AccessRelevance tagging machine
Author(s) -
Dmitry Molchanov,
D. A. Kondrashkin,
Dmitry Vetrov
Publication year - 2015
Publication title -
machine learning and data analysis
Language(s) - English
Resource type - Journals
ISSN - 2223-3792
DOI - 10.21469/22233792.1.13.09
Subject(s) - relevance (law) , computer science , feature selection , artificial intelligence , binary classification , binary number , machine learning , feature (linguistics) , bayesian probability , selection (genetic algorithm) , data mining , pattern recognition (psychology) , support vector machine , mathematics , philosophy , arithmetic , law , political science , linguistics
In many classification or regression problems, there may be a lot of irrelevant features. Bayesian automatic relevance determination (ARD) is a popular approach to feature selection. However, the application area of this approach has been limited. In this paper, this approach is utilized in a more general case and it is applied to a binary classification problem with binary features. Also, a new binary classification model and a learning algorithm that can purge unwanted features from the model have been developed.
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