Computer Science ›› 2018, Vol. 45 ›› Issue (1): 307-312.doi: 10.11896/j.issn.1002-137X.2018.01.053

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Selective Ensemble Learning Human Activity Recognition Model Based on Diversity Measurement Cluster

WANG Zhong-min, ZHANG Shuang and HE Yan   

  • Online:2018-01-15 Published:2018-11-13

Abstract: To improve the accuracy of human activity recognition based on mobile phone,and optimize the generalization performance of multiple classifiers ensemble system and the diversity of individual classifier,an activity recognition model based on selective ensemble learning of diversity measure increment-affinity propagation clustering(DMI-AP) was proposed.Firstly,all the samples are bootstrapped and base classifiers are trained in the training set. The mode selects the base classifiers whose accuracy is greater than the average accuracy. The classifier set consists of the selected classifiers,and then the base classifiers of the training set are chosen to cluster,the double default diversity increment value are got by calculating the double default diversity measure value between base classifiers.The value is clustered by the affinity propagation clustering algorithm and divided into k clusters.Each cluster’s center classifier forms multi-classifier systems.Finally,the outputs of classifiers are fused by calculating the average.The experimental results show that the diversity of individual classifier increases and the searching space of the classifier decreases by using the DMI-AP model.Compared with the traditional Bagging,Adaboost and RF methods,the recognition accuracy of the proposed model is improved by 8.11%.

Key words: Selective ensemble,Diversity measure increment,Affinity propagation clustering,Activity recognition

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