Journal
STATISTICS AND COMPUTING
Volume 19, Issue 3, Pages 317-327Publisher
SPRINGER
DOI: 10.1007/s11222-008-9094-7
Keywords
Ensemble classifier; Bootstrap; Bagging; Random forest; Adaboost; Principal component analysis; Kappa-error diagram
Funding
- National Natural Science Foundation of China [10531030, 60675013]
- National Basic Research Program of China [2007CB311002]
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This paper presents a novel ensemble classifier generation method by integrating the ideas of bootstrap aggregation and Principal Component Analysis (PCA). To create each individual member of an ensemble classifier, PCA is applied to every out-of-bag sample and the computed coefficients of all principal components are stored, and then the principal components calculated on the corresponding bootstrap sample are taken as additional elements of the original feature set. A classifier is trained with the bootstrap sample and some features randomly selected from the new feature set. The final ensemble classifier is constructed by majority voting of the trained base classifiers. The results obtained by empirical experiments and statistical tests demonstrate that the proposed method performs better than or as well as several other ensemble methods on some benchmark data sets publicly available from the UCI repository. Furthermore, the diversity-accuracy patterns of the ensemble classifiers are investigated by kappa-error diagrams.
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