Impact-Learning

Md. Kowsher, Anik Tahabilder, Saydul Akbar Murad

202018 citationsDOI

Abstract

The ultimate goal of this research paper is to introduce a robust machine learning algorithm called Impact-Learning, which is being used widely to achieve more advanced results on many machine-learning related challenges. Impact learning is a supervised machine learning algorithm for resolving classification and linear or polynomial regression knowledge from examples. It also contributes to analyzing systems for competitive data. This algorithm is unique for being capable of learning from a competition, which is the impact of independent features. In other words, it is trained by the impacts of the features from the intrinsic rate of natural increase (RNI). The input to the Impact Learning is a training set of numerical data. In this work, we used six datasets related to regressions and classifications as the experiment of the Impact Learning, and the comparison indicates that at outperforms other standard machine learning regressions and classifications algorithms such as Random forest tree, SVM, Naive Bayes, Logistic regression and so forth.

Topics & Concepts

Machine learningArtificial intelligenceComputer scienceOnline machine learningSupport vector machineNaive Bayes classifierDecision treeRandom forestActive learning (machine learning)Instance-based learningSupervised learningComputational learning theoryArtificial neural networkAnomaly Detection Techniques and ApplicationsTime Series Analysis and ForecastingData Stream Mining Techniques