Abstract
In recent years, machine learning technique has been applied to various problems. The improvement of computational power enables the processing of large scale data in a practical time and brought the success of machine learning technique. However, the processing speed of current machine learning models still have a potential to be improved. We are trying to improve the processing speed of Random Forest, which is known as a fast and reliable classification model. In this study, we propose a Discrimination HyperSpace called DHS, which realized a pseudo Random Forest. Experiment results show our method runs much faster than original Random Forest without losing classification performance.
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Kaneko, T., Akiyma, H., Aramaki, S. (2017). Fast Pseudo Random Forest Using Discrimination Hyperspace. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_40
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DOI: https://doi.org/10.1007/978-3-319-61833-3_40
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