Evaluating Advanced Machine Learning Techniques for Pulsar Detection from HTRU Survey

被引:0
作者
Punia, Akhil [1 ]
Sardana, Ashish [1 ]
Subashini, Monica [1 ]
机构
[1] VIT Univ, Sch Elect Engn, Vellore, Tamil Nadu, India
来源
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SUSTAINABLE SYSTEMS (ICISS 2017) | 2017年
关键词
Machine Learning; HTRU; SVM; Random Forest; Xgboost; SMOTE; Neural Network); SELECTION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
High Time Resolution Universe (HTRU) Survey was conducted to search for Pulsars and Fast Transients using the Parkes Telescope in Australia. Majority of the Pulsars detections were actually false positives caused by radio frequency interference (RFI) and noise. We have used state of the art Machine Learning techniques that have improved significantly in recent years to evaluate feature importance and compare the performances of different approaches to design a binary classifier that automatically labels real Pulsar candidates. We have tried to address the problem of class imbalance by using Synthetic minority oversampling technique (SMOTE) and optimized our models by hyper parameter tuning to maximize accuracy and the geometric mean.
引用
收藏
页码:470 / 474
页数:5
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