A random feature mapping method based on the AdaBoost algorithm and results fusion for enhancing classification performance

•RFM generates multiple feature subsets by random feature mapping for stabilizing.•RFM enhances classification effect by fusing results of multiple feature subsets.•RFM assigns optimal weights to different weak classifiers by AdaBoost algorithm.•RFM processes datasets of different dimensions and dis...

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Bibliographic Details
Published in:	Expert systems with applications Vol. 256; p. 124902
Main Authors:	Shan, Wangweiyi, Li, Dong, Liu, Shulin, Song, Mengmeng, Xiao, Shungen, Zhang, Hongli
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 05.12.2024
Subjects:	AdaBoost algorithm Classification performance Multiple feature subsets Random feature mapping Results fusion Multiple feature subsets Classification performance AdaBoost algorithm Results fusion Random feature mapping
ISSN:	0957-4174
Online Access:	Get full text
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Summary:	•RFM generates multiple feature subsets by random feature mapping for stabilizing.•RFM enhances classification effect by fusing results of multiple feature subsets.•RFM assigns optimal weights to different weak classifiers by AdaBoost algorithm.•RFM processes datasets of different dimensions and distribution adaptively. The feature mapping method can improve data separability, enhance data representation ability, and reduce data processing complexity. However, on the one hand, the existing feature mapping methods have difficulty processing datasets of different dimensions and distributions adaptively, limiting the scope of application; on the other hand, a single feature mapping method has the problem of instability and poor generalization ability, weakening the classification ability of subsequent classifiers. This paper proposes a random feature mapping method based on the AdaBoost algorithm and results fusion to enhance classification performance. The method adopts horizontal expansion, fine-tuning weights through sparse autoencoders, and uses input-mapped features as feature nodes to generate multiple feature subsets for increasing stability. After training weak classifiers on each multiple feature subset, the weights of classifiers are adjusted adaptively by the Adaboost ensemble algorithm. Finally, the method fuses weak classifiers twice to enhance classification performance, which abandons the traditional voting method and uses the weighted probability selection method. Experiments on twenty classic datasets show that the proposed method can effectively mine essential features and enhance classification accuracy compared with original datasets. For instance, the Balance dataset has an average classification accuracy of more than 20% higher than the original dataset on the KNN classifier. The proposed method outperforms alternative feature mapping methods in terms of performance and efficiency on different classifiers in most cases.
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2024.124902