Research on Applied Technology in Experiments with Three Boosting Algorithms

Abstract

Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. In this paper we use applied technology to built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN and Boosting-MultiTAN ensembles with 25 sub-classifiers on standard benchmark datasets and the proposed technique was the most accurate. These results argue that boosting algorithms deserve more attention in machine learning and data mining communities.

Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm

“…Considering the practical problem of classification prediction, the Boosting algorithm offers an effective solution approach. Boosting algorithm is an integrated learning idea that converts weak learners into strong learners by adding iterations, which can solve the supervised learning classification problem (Susnjak et al, 2012;Sun and Zhou, 2014). Currently, Boosting algorithm is widely used in the photovoltaic power generation prediction field (Imran, 2021;Liu et al, 2021;Yamamoto et al, 2022), business forecasting (Kiki and Vinasetan, 2020;Xie et al, 2021), and medical and healthcare (Amy Isabella et al, 2022;Xue, 2022).…”