Improve Ensemble Learning Algorithms in Unbalanced Data
WANG Lu
CHENG Xiaorong
Abstract：In recent years,there is a growing focus on machine learning research,and one of the key points in the field of ma-chine learning is ingestion learning.The basic principle of integrated learning is to use many independent classifiers and adopt a method to fuse them into a strong learner to overcome the shortcomings of single learner classification.Based on the comparison of four algorithms,which are Bagging algorithm,Random Forest algorithm,Weighted KNN(K-NearestNeighbor)algorithm and Ada-Boost algorithm,the weighted KNN algorithm and the AdaBoost algorithm are fused together.The dataset used is the dataset of the shopping behavior of network users.During the experiment,the unbalanced data is first processed using SMOTE sampling,and then the above four algorithms and the improved AdaBoost algorithm are evaluated and compared.Through comparison,it is found that the improved AdaBoost algorithm has better prediction performance.The improved AdaBoost algorithm is computed in parallel on the Spark platform to improve computing efficiency.
Keywords：ensemble learningAdaBoost algorithmSMOTE samplingWeighted KNN(K-NearestNeighbor)algorithmunbalanced dataSpark platform
Publication Date：2025-01-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 26-30 )