Minor Description
For classification tasks involving large feature spaces in the order of tens of thousands or higher (e.g., text classification with n-grams, where n > 1), it is often useful to rank and filter features that are irrelevant thereby reducing the feature space by at least one or two orders of magnitude without impacting performance on key evaluation metrics (accuracy/precision/recall).
A feature evaluation interface which is flexible needs to be designed and at least two methods should be implemented with Information Gain being a priority as it has been shown to be amongst the most reliable.
Special consideration should be taken in the design to account for wrapper methods (see research papers below) which are more practical for lower dimensional data.
Relevant research:
- Brown, G., Pocock, A., Zhao, M. J., & Luján, M. (2012). Conditional
likelihood maximisation: a unifying framework for information theoretic
feature selection.The Journal of Machine Learning Research, 13, 27-66. - Forman, George. "An extensive empirical study of feature selection metrics for text classification." The Journal of machine learning research 3 (2003): 1289-1305.
Attachments
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SPARK-5491 Chi-square feature selection
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