  This paper presents a novel meta learning framework for feature selection
(FS) based on fuzzy similarity. The proposed method aims to recommend the best
FS method from four candidate FS methods for any given dataset. This is
achieved by firstly constructing a large training data repository using data
synthesis. Six meta features that represent the characteristics of the training
dataset are then extracted. The best FS method for each of the training
datasets is used as the meta label. Both the meta features and the
corresponding meta labels are subsequently used to train a classification model
using a fuzzy similarity measure based framework. Finally the trained model is
used to recommend the most suitable FS method for a given unseen dataset. This
proposed method was evaluated based on eight public datasets of real-world
applications. It successfully recommended the best method for five datasets and
the second best method for one dataset, which outperformed any of the four
individual FS methods. Besides, the proposed method is computationally
efficient for algorithm selection, leading to negligible additional time for
the feature selection process. Thus, the paper contributes a novel method for
effectively recommending which feature selection method to use for any new
given dataset.
