  In this paper a hybrid feature selection method is proposed which takes
advantages of wrapper subset evaluation with a lower cost and improves the
performance of a group of classifiers. The method uses combination of sample
domain filtering and resampling to refine the sample domain and two feature
subset evaluation methods to select reliable features. This method utilizes
both feature space and sample domain in two phases. The first phase filters and
resamples the sample domain and the second phase adopts a hybrid procedure by
information gain, wrapper subset evaluation and genetic search to find the
optimal feature space. Experiments carried out on different types of datasets
from UCI Repository of Machine Learning databases and the results show a rise
in the average performance of five classifiers (Naive Bayes, Logistic,
Multilayer Perceptron, Best First Decision Tree and JRIP) simultaneously and
the classification error for these classifiers decreases considerably. The
experiments also show that this method outperforms other feature selection
methods with a lower cost.
