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This paper presents a novel meta learning framework for feature selection(FS) based on fuzzy similarity. The proposed method aims to recommend the bestFS method from four candidate FS methods for any given dataset. This isachieved by firstly constructing a large training data repository using datasynthesis. Six meta features that represent the characteristics of the trainingdataset are then extracted. The best FS method for each of the trainingdatasets is used as the meta label. Both the meta features and thecorresponding meta labels are subsequently used to train a classification modelusing a fuzzy similarity measure based framework. Finally the trained model isused to recommend the most suitable FS method for a given unseen dataset. Thisproposed method was evaluated based on eight public datasets of real-worldapplications. It successfully recommended the best method for five datasets andthe second best method for one dataset, which outperformed any of the fourindividual FS methods. Besides, the proposed method is computationallyefficient for algorithm selection, leading to negligible additional time forthe feature selection process. Thus, the paper contributes a novel method foreffectively recommending which feature selection method to use for any newgiven dataset.

A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity