Abstract / Description of output
Variable selection is a necessary preprocessing stage in many applications, such as regression and classification, to reduce computational cost, to avoid curse of dimensionality and to improve generalization. A filter type approach to variable selection employs statistical criteria such as dependence to quantify the importance of a variable. In this paper we discuss the use of conditional independence as a criteria for variable selection, and describe a forward selection and a backward elimination based approach using this notion. We introduce two measures of conditional independence, describe their respective estimators and apply them in the variable selection task. We also provide a brief overview of the available variable selection methods and compare the proposed methods with these methods.
Original language | English |
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Title of host publication | Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on |
Pages | 456-461 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 29 Aug 2010 |