Abstract
This paper deals with the problem of integrating the most suitable feature selection methods for a given problem in order to achieve the best feature order. A new, adaptive and hybrid feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution. Various state-of-the-art feature selection methods are presented in detail with examples of their applications and an exhaustive evaluation is conducted to measure and compare the their performance with the proposed approach. Results prove that while the individual feature selection methods may perform with high variety on the test cases, the combined algorithm steadily provides noticeably better solution.
Highlights
Most real world modeling problems can be formulated as estimation of some numerical value or classifying a given number of samples
Adaptivity of the proposed algorithm is realized in such a way that at an individual step of the feature selection algorithm it iterates in the space of the variables but in the space of available features selection techniques, too
Known linear functions were used for test data generation and additional effects were given to this data by varying its distributions, noise and outlier levels for analyze their effects on the performance of the AHFS
Summary
Most real world modeling problems can be formulated as estimation of some numerical value or classifying a given number of samples. These problems, more often than not, are very complex and can be defined by tens, hundreds and even thousands of variables. In order to achieve a more generalized solution It minimizes the shortcomings of each incorporated algorithms by choosing dynamically the most suitable one for a given assignment and dataset. In the paragraph the proposed method is evaluated through many benchmarking datasets according to modelling error and calculation time demends, and it is compared by the very recent, state-of-the-art feature selection methods.
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