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

[1] CAGATAY C,SELIN T,ELIF P,et al.On the use of ensemble of classifiers for accelerometer based activity recognition[J].ELS-EVIER Applied Soft Computing,2015,7(C):1018-1022.
[2] CHETTY G,WHITE M,AKTHER F.Smart Phone Based Data Mining for Human Activity Recognition [J].Procedia Computer Science,2015,6:1181-1187.
[3] JUREK A,NUGENT C,BI Y,et al(1)Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes[J].Sensors,2014,14(7):12285-12304.
[4] HENG X,WANG Z M.Human Activity Recognition Based on Accelerometer Data from a Mobile Phone[J].Journal of Xi’an University of Posts and Telecommunications,2014,19(6):76-79.(in Chinese) 衡霞,王忠民.基于手机加速度传感器的人体行为识别[J].西安邮电大学学报,2014,9(6):76-79.
[5] WANG Z M,CAO D.A Feature Selection Method for Behavior Recognition Based on Ant Colony Algorithm[J].Journal of Xi’an University of Posts and Telecommunications,2014,9(1):73-77.(in Chinese) 王忠民,曹栋.基于蚁群算法的行为识别特征优选方法[J].西安邮电大学学报,2014,9(1):73-77.
[6] TANG C,WANG W J,LI W,et al.Human Action Recognition Algorithm Based on Selective Ensemble Rotation Forest[J].Pattern Recognition and Artificial Intelligence,2016,9(4):313-321.(in Chinese) 唐超,王文剑,李伟,等.基于选择性集成旋转森林的人体行为识别算法[J].模式识别与人工智能,2016,9(4):313-321.
[7] WANG Z M,WANG B.Feature Selection Method for Moblie User Behavior Recognition Based on Multiband Time Domain Decomposition[J].Application Research of Computers,2015,32(7):1956-1958.(in Chinese) 王忠民,王斌.多频段时域分解的行为识别特征优选方法[J].计算机应用研究,2015,2(7):1956-1958.
[8] LI H,DING S F.AP Twice Clustering Based Neural NetworkEnsemble Algorithm[J].Computer Science,2015,42(2):224-227.(in Chinese) 李辉,丁世飞.基于AP二次聚类的神经网络集成算法研究[J].计算机科学,2015,2(2):224-227.
[9] YUAN Y,WANG C,ZHANG J Z,et al(1)An Ensemble Approach for Activity Recognition with Accelerometer in Mobile-phone[C]∥Proceedings of the 17th IEEE International Conference on Computational Science and Engineering (CSE).2014:1469-1474.
[10] ZHANG C X,ZHANG J S.A Review of the Selective Ensemble Learning Algorithm [J].Journal of Computer Science,2011,34 (8):1399-1410.(in Chinese) 张春霞,张讲社.选择性集成学习算法综述[J].计算机学报,2011,4(8):1399-1407.
[11] ROONEY N,PATTERSON D,NUGENT C.Non-strict heterogeneous Stacking[C]∥ International Workshop.2009:478-487.
[12] ZHANG C X,DUIN R P.An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets[C]∥ International Workshop on Multiple Classifier Systems.2009:478-487.
[13] ZHOU Z H,CHEN S F.Neural Network Ensemble[J].Chinese Journal of Computers,2002,5(1):1-8.
[14] LAZAREVIC A,OBRADOVIC Z.Effective pruning of neural network classifier ensembles[J].International Joint Conference on Neural Networks,2001(2):796-801.
[15] FREY B J,DUECK D.Clustering by Passing Messages Between Data Points[J].Science,2007,5(5814):927-976.
[16] KUNCHEVA L I,WHITAKER C J.Measures of Diversity in Classifier Ensembles and Their Relationship with Ensemble Accuray [J].Machine Learning,2003,1(2):187-207.
[17] DENG W Y,ZHENG Q H,WANG Z M.Cross-person activity recognition using reduced kernel extreme learning machine[J].ELSEVIER Neural Networks,2014,3(5):1-7.

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