Abstract Due to the redundancy and the noise in high-dimensional data,a covering model constructed from these data can not reflect their distribution information,which leads to the performance degradation of one-class classifiers. To solve this problem,a pruning random subspace ensemble multi-spanning tree method is proposed. Firstly,several random subspaces are created,and minimum spanning tree covering models are constructed in each subspace respectively. Next,pruning ensembles are applied to each classifier by using an evaluation criterion. Finally,these subspace classifiers are integrated into an ensemble classifier by mean combining. Experimental results show that the proposed covering classifier by ensemble multi-trees has a better correct rate in classification than other direct covering classifiers and bagging algorithm.
HU Zheng-Ping,LIU Kai. One-Class Classifier Algorithm Based on Ensemble Multi-Spanning Trees by Pruning Random Subspace Method[J]. , 2013, 26(4): 351-356.
[1] Li Wenkai,Guo Qinghua,Elkan C. A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data. IEEE Trans on Geoscience and Remote Sensing,2011,49(2): 717-725 [2] Kang I,Jeong M K,Kong D. A Differentiated One-Class Classification Method with Applications to Intrusion Detection. Expert Systems with Applications,2012,39(4): 3899-3905 [3] Pauwels E J,Ambekar O. One Class Classification for Anomaly Detection: Support Vector Data Description Revisited // Proc of the 11th International Conference on Advances in Data Mining: Applications and Theoretical Aspects.Amsterdam,Netherlands,2011: 25-39 [4] Wang Taiyue,Chiang H M. One-Against-One Fuzzy Support Vector Machine Classifier: An Approach to Text Categorization. Expert Systems with Applications,2009,36(6): 10030-10034 [5] Dixit M,Rasiwasia N,Vasconcelos N. Adapted Gaussian Models for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs,USA,2011: 937-943 [6] Chen Xuefeng,Liu Xiabin,Jia Yunde. Discriminative Structure Selection Method of Gaussian Mixture Models with Its Application to Handwritten Digit Recognition. Neurocomputing,2011,74(6): 954-961 [7] Veon K L,Mahoor M H. Localized Support Vector Machines Using Parzen Window for Incomplete Sets of Categories // Proc of the IEEE Workshop on Applications of Computer Vision. Kona,USA,2011: 448-454 [8] Cyganek B. One-Class Support Vector Ensembles for Image Segmentation and Classification. Journal of Mathematical Imaging and Vision,2012,42(2/3): 103-117 [9] Tax D M J,Duin R P W. Support Vector Data Description. Machine Learning,2004,54(1): 45-56 [10] Juszczak P,Tax D P W,Pekalska E,et al. Minimum Spanning Tree Based One-Class Classifier. Neurocomputing,2009,72(7/8/9): 1859-1869 [11] Dietterich T G. Ensemble Methods in Machine Learning // Proc of the 1st International Workshop on Multiple Classifier Systems. London,UK,2000: 1-15 [12] Breiman L. Bagging Predictors. Machine Learning,1996,24(2): 123-140 [13] Schapire R E. The Strength of Weak Learnability. Machine Learning,1990,5(2): 197-227 [14] Ho T K,Labs B,Hill M,et al. The Random Subspace Method for Constructing Decision Forests. IEEE Trans on Pattern Analysis and Machine Intelligence,1998,20(8): 832-844 [15] Tumer K,Ghosh J. Classifier Combining: Analytical Results and Implications // Proc of the AAAI Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms. Portland,USA,1996: 126-132 [16] Plumpton C O,Kuncheva L I,Oosterhof N N,et al. Naive Random Subspace Ensemble with Linear Classifiers for Real-Time Classification of FMRI Data. Pattern Recognition,2012,45(6): 2101-2108 [17] Bjǒrnsdotter M,Wessberg J. Clustered Sampling Improves Random Subspace Brain Mapping. Pattern Recognition,2012,45(6): 2035-2040 [18] Segui S,Igual L,Vitria J. Weighted Bagging for Graph Based One-Class Classifiers // Proc of the 9th International Workshop on Multiple Classifier Systems. Cairo,Egypt,2010: 1-10 [19] Cheplygina V,Tax D. Pruned Random Subspace Method for One-Class Classifiers // Proc of the 10th International Workshop on Multiple Classifier Systems. Naples,Italy,2011: 96-105 [20] Yu Guoxian,Zhang Guoji,Wei Jia,et al. A Multi Graphs Based Transductive Ensemble Classification Method. Journal of Electronics and Information Technology,2011,33(8): 1884-1888(in Chinese) (余国先,张国基,韦 佳,等.一种基于多图的集成直推分类方法.电子与信息学报,2011,33(8): 1884-1888)
2013, Vol. 26 
