A weighted wrapper approach to feature selection - Kusy, Maciej; Zajdel, Roman (Zielona Góra: Uniwersytet Zielonogórski)
This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson`s linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion.
Keywords: feature selection; wrapper approach; feature significance; weighted combined ranking; convolutional neural networks; classification accuracy