A Hybrid Ensemble Algorithm Combining AdaBoost and Genetic Algorithm for Cancer Classification with Gene Expression Data

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 18; no. 3; pp. 863 - 870
• Main Authors: Lu, Huijuan, Gao, Huiyun, Ye, Minchao, Wang, Xiuhui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AdaBoost; Algorithms; Bayes Theorem; Bayesian analysis; Cancer; Classification; Classification algorithms; Classifiers; Computational Biology - methods; Data processing; decision group; decision tree; Decision Trees; Diversity reception; Gene expression; Gene Expression Profiling; genetic algorithm; Genetic algorithms; Humans; K-nearest neighbor; Machine learning; Naïve Bayes; Neoplasms - diagnosis; Neoplasms - genetics; Neoplasms - metabolism; Prediction algorithms; Training; Transcriptome - genetics
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2019.2952102

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM, and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.