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Hello, I am trying to do text classification using Naive Bayes. Before training, I would like to make feature selection in order to reduce the feature space dimension. In order to do so, I have thought of using a method that weights 2 filters for scoring the features and then select the top K features. For example, let's suppose that I have Information Gain as the first filter, and "X" as the second filter. I would like to find the best weights a, b as follows: score(feature) = a * Infogain(feature) + b * X(feature) I guess one possible option should be trying with some values and see if the performance gets improved, but there are any other less costly methods ? (for example, I thought of classifying a feature as good or bad using svm: a manual annotator classifies some features as good or bad ("label of the feature"), and Infogain(feature), X(feature) are used as "features or the feature" ) Thanks in advance... |
