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Using Ensemble Methods to Solve the Problem of Pulsar Search

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Big Data and Networks Technologies (BDNT 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 81))

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Abstract

Ensemble methods are a machine learning technique that combines several base models in order to produce one optimal predictive model. In this paper, we compare accuracy metric of three ensemble methods: Bagging, Random Forest, and Boosting. Then, We use the “CARET package”, implemented in R language, to experiment the Time Resolution Universe (HTRU2) dataset, obtained from UCI Machine Learning Repository.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets.html.

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Authors and Affiliations

  1. Laboratory of Engineering Sciences and Modeling, Faculty of Sciences, Ibn Tofail University, Campus Universitaire, BP 133, Kenitra, Morocco

    Mourad Azhari, Altaf Alaoui & Jamal Zerouaoui

  2. Laboratoire de Modélisation Mathématiques et de Calculs Economiques, FSJES, Université Hassan 1er, Settat, Morocco

    Abdallah Abarda

  3. Laboratory of Research in Computer Science, Data Sciences and Knowledge Engineering, Department of Data, Content and knowledge Engineering, School of Information Sciences, Rabat, Morocco

    Badia Ettaki

Authors
  1. Mourad Azhari
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  2. Altaf Alaoui
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  3. Abdallah Abarda
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  4. Badia Ettaki
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  5. Jamal Zerouaoui
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Corresponding author

Correspondence to Mourad Azhari .

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Editors and Affiliations

  1. Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia, Morocco

    Yousef Farhaoui

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Azhari, M., Alaoui, A., Abarda, A., Ettaki, B., Zerouaoui, J. (2020). Using Ensemble Methods to Solve the Problem of Pulsar Search. In: Farhaoui, Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_14

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