Impact Learning: A Learning Method from Features Impact and Competition

TL;DR

Impact Learning is a novel supervised machine learning algorithm that leverages feature impact and competition effects, demonstrating superior performance in classification and regression tasks involving competitive data.

Contribution

The paper introduces Impact Learning, a new algorithm that learns from feature impacts and competition effects, enhancing analysis of competitive datasets.

Findings

Impact Learning outperforms conventional algorithms.

Effective in classification and regression tasks.

Utilizes intrinsic natural increase rate effects.

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.