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Title: INVESTIGATION OF PULSAR STARS ASTRONOMICAL DATASET BY MEANS OF MACHINE LEARNING ALGORITHMS
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INVESTIGATION OF PULSAR STARS ASTRONOMICAL DATASET BY MEANS OF MACHINE LEARNING ALGORITHMS
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1314-2704
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English
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20
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2.1
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The ?Predicting a Pulsar Star? dataset has been investigated in this paper. The pulsars
are special neutron stars emitting narrow beams of light with high energy into space. They rotate very rapidly and the signal repeats. Scientists seek periodic radio signals in order to detect these objects. Of course, frequency patterns vary from one star to another. The signal is averaged on a lot of rotations. At the same time a lot of pulsar candidates are just radio noise. There is a lot of objects to check. In this task machine learning algorithms can be applied to remove objects that definitely do not belong to pulsars. This step allows reducing time to handle signals of potential pulsars. Astronomers could check only ?difficult? cases for classification. Other objects are supposed as pulsars registered with high probability. Review of literature has shown that usually only one algorithm is used in each paper on astronomy objects detection or too complex approaches are applied. For example, training neural nets takes a lot of time and it needs attention of specialists. In this research simple classification algorithms are applied to this task: the Naive Bayes, the logistic regression and the CART decision tree classifiers. The ensemble methods are applied at the extended dataset and its transformed version by means of principal component analysis with polynomial kernels. The random forest ensemble method is used. Averaged accuracy values of the constructed classifiers are about 93%. Better accuracy cannot be achieved because of high level of noise in the dataset. |
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conference
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20th International Multidisciplinary Scientific GeoConference SGEM 2020
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20th International Multidisciplinary Scientific GeoConference SGEM 2020, 18 - 24 August, 2020
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts & Letters; Acad Fine Arts Zagreb Croatia; C
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199-206
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18 - 24 August, 2020
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website
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cdrom
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6987
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Pulsar; star; classification; noise; decision tree; random forest; decision tree;
Naive Bayes; logistic regression |
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