Data size plays a significant role in the design and the performance of data mining models. A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy. Features selection algorithms aim at selecting the best features and eliminating unnecessary ones, which in turn simplifies the structure of the data mining model as well as increases its performance. This paper introduces a robust features selection algorithm, named Features Ranking Voting Algorithm FRV. It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based on the feature ranks and voting algorithm. The FRV comprises of three different proposed techniques to select the minimum best feature set, the forward voting technique to select the best high ranks features, the backward voting technique, which drops the low ranks features (low importance feature), and the third technique merges the outputs from the forward and backward techniques to maximize the robustness of the selected features set. Different data mining models were built using obtained selected features sets from applying the proposed FVR on different datasets; to evaluate the success behavior of the proposed FRV. The high performance of these data mining models reflects the success of the proposed FRV algorithm. The FRV performance is compared with other features selection algorithms. It successes to develop data mining models for the Hungarian CAD dataset with Acc. of 96.8%, and with Acc. of 96% for the Z-Alizadeh Sani CAD dataset compared with 83.94% and 92.56% respectively in [48].
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