Feature selection in meta learning framework

Feature selection is a key step in data mining. Unfortunately, there is no single feature selection method that is always the best and the data miner usually has to experiment with different methods using a trial and error approach, which can be time consuming and costly especially with very large datasets. Hence, this research aims to develop a meta learning framework that is able to learn about which feature selection methods work best for a given data set. The framework involves obtaining the characteristics of the data and then running alternative feature selection methods to obtain their performance. The characteristics, methods used and their performance provide the examples which are used by a learner to induce the meta knowledge which can then be applied to predict future performance on unseen data sets. This framework is implemented in the Weka system and experiments with 26 data sets show good results.

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