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Using machine learning with PySpark and MLib for solving a binary classification problem: case of searching for exotic particles. (English) Zbl 07438702

Melliani, Said (ed.) et al., Recent advances in intuitionistic fuzzy logic systems and mathematics. Selected papers based on the presentations at the 6th international congress of the Moroccan Society of Applied Mathematics, Beni Mellal, Morocco, November 7–9, 2019. Cham: Springer. Stud. Fuzziness Soft Comput. 395, 109-118 (2021).
Summary: Searching for exotic particles in high-energy represents a major challenge for physicists. In this paper, we propose to solve the binary classification problem in the area of exotic particles using the Apache Spark environment with the Mlib library. Then, We compare the performance of four methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). In this work, we use “SUSY” dataset, collected from UCI machine learning repository, for the experimentation phase.
For the entire collection see [Zbl 1468.68017].

MSC:

68-XX Computer science
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