We first identified the most informative unigrams and bigrams using the information gain measure ( Yang and Pedersen 1997 ) , and then selected only the positive outcome predictors using odds ratio ( Mladenic and Grobelnik 1999 ) .
feature selection for unbalanced class distribution and naive bayes
Dunja Mladenic Marko Grobelnik
The n-gram based classifier is also a naive Bayes classifier , but it operates on a different set of features . We first identified the most informative unigrams and bigrams using the information gain measure ( Yang and Pedersen 1997 ) , and then selected only the positive outcome predictors using odds ratio ( Mladenic and Grobelnik 1999 ) . Diseasespecific terms , such as rheumatoid arthritis , were then manually removed .
We first identified the most informative unigrams and bigrams using the information gain measure ( Yang and Pedersen 1997 ) , and then selected only the positive outcome predictors using odds ratio ( @@CITATION ) .