An Intelligent Fusion Algorithm and Its Application Based on Subgroup Migration and Adaptive Boosting

Symmetry

卷:13

Li Kewen¹  Yang Lei¹  Zhai Jiannan²  Li Timing³ 

[1] College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China;

[2] Institute for Sensing and Embedded Network Systems Engineering, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA;

[3] School of Microelectronics, Tianjin University, Tianjin 300072, China;

关键词: fusion algorithm;    imbalanced data;    feature redundancies;    subgroup migration;    adaptive boosting;   

DOI  :  10.3390/sym13040569

来源: DOAJ

【 摘 要 】

Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. To resolve these problems, we propose an intelligent fusion algorithm, SMPSO-HS-AdaBoost, which combines particle swarm optimization based on subgroup migration and adaptive boosting based on hybrid-sampling. In this paper, we apply the proposed intelligent fusion algorithm to software defect prediction to improve the prediction efficiency and accuracy by solving the issues caused by imbalanced data and feature redundancies. The results show that the proposed algorithm resolves the coexisting problems of imbalanced data and feature redundancies, and ensures the efficiency and accuracy of software defect prediction.

【 授权许可】

Unknown