Abstract
The amount of data in the world is growing exponentially due to the elevated number of applications in the most various contexts. This data needs to be analyzed in order to extract valuable underlying information from them. Machine learning is a useful tool to do this task, but the high complexity of the data forces to use other methods to reduce such complexity. Dimensionality reduction (feature selection) is one of the most used method to achieve this goal. As usual, many algorithms were proposed to reduce dimension of data, each one with its own advantages and drawbacks. The variety of algorithms usually makes researches to test several methods and choose the best solution. Based on that, this paper proposes a combination of feature selection algorithms in order to create a single and more stable solution. We tested this approach using real datasets and machine learning algorithms. Results showed we can use the combined solution with little or none loss in classification accuracy. So, our method can be used as a stable choice when there is few knowledge about the problem.
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Acknowledgments
This paper was partially supported by CNPq Universal Grant no 480997/2013-6 and UFRN scholarship program.
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Araújo, D., Jesus, J., Neto, A.D., Martins, A. (2016). A Combination Method for Reducing Dimensionality in Large Datasets. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_46
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DOI: https://doi.org/10.1007/978-3-319-44781-0_46
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