Application of adaptive ensemble algorithm based on
correctness and diversity
LUO Jian-hong1,2, CHEN De-zhao1
1.Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China;
2.Department of Management Science and Engineering, Zhejiang SciTech University, Hangzhou 310018, China
A new ensemble algorithm was proposed. to select individual classifier reasonably from ensemble classifier in order to improve the effect of ensemble learning. Designs a selective index giving consideration to correctness and diversity based on knowledge granular principle, in order to fast select some individuals from the trained classifiers to build 'classifiers space’. Then each specific ensemble classifier is generated for each class by adaptive strategy, and these ensemble classifiers are inclusive, so the group of ensemble classifiers would cost little computation resource. Then make classification decision by adaptive strategy. Experiments conducted on some typical classification problems demonstrate that compared to the other ensemble methods, this algorithm is higher efficient, more stable and has stronger generalization performance．
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