Semantic Scholar Open Access 2020 17 sitasi

Higgs Boson Discovery using Machine Learning Methods with Pyspark

Mourad Azhari Abdallah Abarda B. Ettaki J. Zerouaoui Mohamed Dakkon

Lihat Sumber DOI

Abstrak

Abstract Higgs Boson is an elementary particle that gives the mass to everything in the natural world. The discovery of the Higgs Boson is a major challenge for particle physics. This paper proposes to solve the Higgs Boson Classification Problem with four Machine Learning (ML) Methods, using the Pyspark environment: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT). We compare the accuracy and AUC metrics of those ML Methods. We use a large dataset as Higgs Boson, collected from public site UCI and Higgs dataset downloaded from Kaggle site, in the experimentation stage.

Topik & Kata Kunci

Computer Science

Penulis (5)

Mourad Azhari

Abdallah Abarda

B. Ettaki

J. Zerouaoui

Mohamed Dakkon

Format Sitasi

APA MLA BibTeX

Azhari, M., Abarda, A., Ettaki, B., Zerouaoui, J., Dakkon, M. (2020). Higgs Boson Discovery using Machine Learning Methods with Pyspark. https://doi.org/10.1016/j.procs.2020.03.053

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.procs.2020.03.053

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 17×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.procs.2020.03.053
Akses: Open Access ✓