Detection of Pulsar Candidates using Bagging Method

被引：10

作者：

Azhari, Mourad ^{[1
]}

Abarda, Abdallah ^{[2
]}

Alaoui, Altaf ^{[1
]}

Ettaki, Badia ^{[3
]}

Zerouaoui, Jamal ^{[1
]}

机构：

[1] Ibn Tofail Univ, Lab Engn Sci & Modeling, Fac Sci, Campus Univ,BP 133, Kenitra, Morocco

[2] Univ Hassan 1er, Lab Modelisat Math & Calculs Econ, FSJES, Settat, Morocco

[3] Sch Informat Sci Rabat, Dept Data Content & Knowledge Engn, Lab Res Comp Sci Data Sci & Knowledge Engn, Rabat, Morocco

来源：

11TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 3RD INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS | 2020年 / 170卷

关键词：

Bagging; CVM; ANN; KNN; CDT; AUC; Error Rate; Computation Time(CT); ARTIFICIAL NEURAL-NETWORK; CLASSIFICATION; SELECTION; SVM;

D O I：

10.1016/j.procs.2020.03.062

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：1096 / 1101

页数：6

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