Impact learning: A learning method from feature’s impact and competition

Prottasha, Nusrat Jahan and Murad, Saydul Akbar and Muzahid, Abu Jafar Md 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)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Asthma prediction; Classification; Diabetes prediction; Heart disease identification; Impact learning; Machine learning; Regression
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 04 Sep 2023 03:20
Last Modified: 04 Sep 2023 03:20
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