An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification

Adaptive boost (AdaBoost) is a prominent example of an ensemble learning algorithm that combines weak classifiers into strong classifiers through weighted majority voting rules. AdaBoost’s weak classifier, with threshold classification, tries to find the best threshold in one of the data dimensions,...

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Vydáno v:Applied sciences Ročník 12; číslo 12; s. 5872
Hlavní autoři: Ding, Yi, Zhu, Hongyang, Chen, Ruyun, Li, Ronghui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2022
Témata:
Accuracy
AdaBoost
Algorithms
Classification
Data mining
generalization
Image retrieval
Machine learning
Multiple Thresholds Classification
ISSN:2076-3417, 2076-3417
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Shrnutí:Adaptive boost (AdaBoost) is a prominent example of an ensemble learning algorithm that combines weak classifiers into strong classifiers through weighted majority voting rules. AdaBoost’s weak classifier, with threshold classification, tries to find the best threshold in one of the data dimensions, dividing the data into two categories-1 and 1. However, in some cases, this Weak Learning algorithm is not accurate enough, showing poor generalization performance and a tendency to over-fit. To solve these challenges, we first propose a new Weak Learning algorithm that classifies examples based on multiple thresholds, rather than only one, to improve its accuracy. Second, in this paper, we make changes to the weight allocation scheme of the Weak Learning algorithm based on the AdaBoost algorithm to use potential values of other dimensions in the classification process, while the theoretical identification is provided to show its generality. Finally, comparative experiments between the two algorithms on 18 datasets on UCI show that our improved AdaBoost algorithm has a better generalization effect in the test set during the training iteration.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app12125872