Impact Learning: A Learning Method from Features Impact and Competition

• URL: http://arxiv.org/abs/2211.02263v1
• Date: Fri, 4 Nov 2022 04:56:35 GMT
• Title: Impact Learning: A Learning Method from Features Impact and Competition
• Authors: Nusrat Jahan Prottasha, Saydul Akbar Murad, Abu Jafar Md Muzahid, Masud Rana, Md Kowsher, Apurba Adhikary, Sujit Biswas, Anupam Kumar Bairagi
• Abstract summary: 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 is prepared by the impacts of the highlights from the intrinsic rate of natural increase.
• Score: 1.3569491184708429
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 wellknown 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 the impact learning over the conventional machine learning algorithm.

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