Adaptive, Hybrid Feature Selection (AHFS) | 数据学习(DataLearner)

Highlights：

• A new feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution.

• A hybrid solution is proposed in the paper which combines the given, available (supervised, state-of-the-art) feature selection techniques that have their own specific, but fixed feature evaluation measures/metrics.

• Adaptivity of the proposed algorithm is realized in such a way that at an individual step of the feature selection algorithm it iterates not only in the space of the variables but in the space of available features selection techniques, too.

• Different directions of tests were applied: linear and non-linear dependencies with varying data distribution, noise and outliers; using benchmark datasets of the UCI Machine Learning Repository and also own real-life datasets; comparison to recent state-of-the-art feature selection methods.

• TThe proposed AHFS nearly doubles the accuracy (resulting in around half value for the related modeling error) compared to the individual methods, making it a superior feature selection algorithm.

摘要

•A new feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution.•A hybrid solution is proposed in the paper which combines the given, available (supervised, state-of-the-art) feature selection techniques that have their own specific, but fixed feature evaluation measures/metrics.•Adaptivity of the proposed algorithm is realized in such a way that at an individual step of the feature selection algorithm it iterates not only in the space of the variables but in the space of available features selection techniques, too.•Different directions of tests were applied: linear and non-linear dependencies with varying data distribution, noise and outliers; using benchmark datasets of the UCI Machine Learning Repository and also own real-life datasets; comparison to recent state-of-the-art feature selection methods.•TThe proposed AHFS nearly doubles the accuracy (resulting in around half value for the related modeling error) compared to the individual methods, making it a superior feature selection algorithm.