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

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 18; číslo 3; s. 863 - 870
Hlavní autoři: Lu, Huijuan, Gao, Huiyun, Ye, Minchao, Wang, Xiuhui
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2019.2952102