Impact learning : A learning method from feature’s impact and competition
Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known m...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier B.V.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40745/1/Impact%20learning_A%20learning%20method%20from%20feature%E2%80%99s%20impact.pdf http://umpir.ump.edu.my/id/eprint/40745/2/Impact%20learning_A%20learning%20method%20from%20feature%E2%80%99s%20impact%20and%20competition_ABS.pdf |
| _version_ | 1825815550462263296 |
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| author | Prottasha, Nusrat Jahan Murad, Saydul Akbar Abu Jafar, Md Muzahid Rana, Masud Kowsher, Md Adhikary, Apurba Biswas, Sujit Bairagi, Anupam Kumar |
| author_facet | Prottasha, Nusrat Jahan Murad, Saydul Akbar Abu Jafar, Md Muzahid Rana, Masud Kowsher, Md Adhikary, Apurba Biswas, Sujit Bairagi, Anupam Kumar |
| author_sort | Prottasha, Nusrat Jahan |
| collection | UMP |
| description | Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm. |
| first_indexed | 2024-09-25T03:48:26Z |
| format | Article |
| id | UMPir40745 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:48:26Z |
| publishDate | 2023 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | UMPir407452024-05-28T07:56:24Z http://umpir.ump.edu.my/id/eprint/40745/ Impact learning : A learning method from feature’s impact and competition Prottasha, Nusrat Jahan Murad, Saydul Akbar Abu Jafar, Md Muzahid Rana, Masud Kowsher, Md Adhikary, Apurba Biswas, Sujit Bairagi, Anupam Kumar QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm. Elsevier B.V. 2023-05 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40745/1/Impact%20learning_A%20learning%20method%20from%20feature%E2%80%99s%20impact.pdf pdf en http://umpir.ump.edu.my/id/eprint/40745/2/Impact%20learning_A%20learning%20method%20from%20feature%E2%80%99s%20impact%20and%20competition_ABS.pdf Prottasha, Nusrat Jahan and Murad, Saydul Akbar and Abu Jafar, Md Muzahid and Rana, Masud and Kowsher, Md and Adhikary, Apurba and Biswas, Sujit and Bairagi, Anupam Kumar (2023) Impact learning : A learning method from feature’s impact and competition. Journal of Computational Science, 69 (102011). pp. 1-10. ISSN 1877-7503. (Published) https://doi.org/10.1016/j.jocs.2023.102011 https://doi.org/10.1016/j.jocs.2023.102011 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Prottasha, Nusrat Jahan Murad, Saydul Akbar Abu Jafar, Md Muzahid Rana, Masud Kowsher, Md Adhikary, Apurba Biswas, Sujit Bairagi, Anupam Kumar Impact learning : A learning method from feature’s impact and competition |
| title | Impact learning : A learning method from feature’s impact and competition |
| title_full | Impact learning : A learning method from feature’s impact and competition |
| title_fullStr | Impact learning : A learning method from feature’s impact and competition |
| title_full_unstemmed | Impact learning : A learning method from feature’s impact and competition |
| title_short | Impact learning : A learning method from feature’s impact and competition |
| title_sort | impact learning a learning method from feature s impact and competition |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/40745/1/Impact%20learning_A%20learning%20method%20from%20feature%E2%80%99s%20impact.pdf http://umpir.ump.edu.my/id/eprint/40745/2/Impact%20learning_A%20learning%20method%20from%20feature%E2%80%99s%20impact%20and%20competition_ABS.pdf |
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