A Modified Random Forest Based on Kappa Measure and Binary Artificial Bee Colony Algorithm

Random forest (RF) is an ensemble classifier method, all decision trees participate in voting, some low-quality decision trees will reduce the accuracy of random forest. To improve the accuracy of random forest, decision trees with larger degree of diversity and higher classification accuracy are se...

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Vydáno v:IEEE access Ročník 9; s. 117679 - 117690
Hlavní autoři: Zhang, Chen, Wang, Xiaofeng, Chen, Shengbing, Li, Hong, Wu, Xiaoxuan, Zhang, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Artificial bee colony algorithm
Datasets
Decision trees
Forestry
Haze
haze prediction
Kappa measure
Optimization algorithms
Performance enhancement
Radio frequency
Random forest
Random forests
Rank tests
Search algorithms
Swarm intelligence
Training
Vegetation
Voting
ISSN:2169-3536, 2169-3536
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Shrnutí:Random forest (RF) is an ensemble classifier method, all decision trees participate in voting, some low-quality decision trees will reduce the accuracy of random forest. To improve the accuracy of random forest, decision trees with larger degree of diversity and higher classification accuracy are selected for voting. In this paper, the RF based on Kappa measure and the improved binary artificial bee colony algorithm (IBABC) are proposed. Firstly, Kappa measure is used for pre-pruning, and the decision trees with larger degree of diversity are selected from the forest. Then, the crossover operator and leaping operator are applied in ABC, and the improved binary ABC is used for secondary pruning, and the decision trees with better performance are selected for voting. The proposed method (Kappa+IBABC) are tested on a quantity of UCI datasets. Computational results demonstrate that Kappa+IBABC improves the performance on most datasets with fewer decision trees. The Wilcoxon signed-rank test is used to verify the significant difference between the Kappa+IBABC method and other pruning methods. In addition, Chinese haze pollution is becoming more and more serious. This proposed method is used to predict haze weather and has achieved good results.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3105796