Detection of Pulsar Candidates using Bagging Method | Zendy

Mourad Azhari | Zendy; Abdallah Abarda | Zendy; Altaf Alaoui | Zendy; Badia Ettaki | Zendy; Jamal Zerouaoui | Zendy

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Detection of Pulsar Candidates using Bagging Method

Author(s) -

Mourad Azhari,

Abdallah Abarda,

Altaf Alaoui,

Badia Ettaki,

Jamal Zerouaoui

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.03.062

Subject(s) - pulsar , computer science , decision tree , artificial intelligence , artificial neural network , computation , machine learning , support vector machine , ensemble learning , pattern recognition (psychology) , tree (set theory) , data mining , algorithm , astrophysics , physics , mathematical analysis , mathematics

The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.

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