|
|
- 2018
一种基于聚类的过抽样算法
|
Abstract:
摘要： 在过抽样技术研究中,为了合成较有意义的新样本,提出一种基于聚类的过抽样算法ClusteredSMOTE-Boost。过滤小类的噪声样本,将剩余的每个小类样本作为目标样本参与合成新样本。对整个训练集聚类,根据聚类后目标样本所在簇的特点确定其权重及合成个数。将所有目标样本聚类,在目标样本所在的簇内选取K个近邻,并从中任选一个与目标样本合成新样本,使新样本与目标样本簇内的样本尽量相似,并减少由于添加样本而造成的边界复杂度。试验结果表明,ClusteredSMOTE-Boost算法在各个度量上均明显优于SMOTE-Boost、ADASYN-Boost和BorderlineSMOTE-Boost三种经典算法。
Abstract: In the research of over sampling, in order to generate meaningful new samples, the ClusteredSMOTE-Boost was proposed, which was based on the clustering technique. The algorithm filtered the noisy of minority class samples and took the remaining minority class samples as target samples to synthesize new samples. According to characteristics of the cluster of target samples after clustering determined the weight and the number of the target samples for the whole training set. All target samples were clustered and K-nearest neighbors in the cluster of the target sample were selected, and then a sample from K-nearest neighbors was randomly chosen to synthesize new sample with target sample. Thus, new samples were similar with samples in the target cluster. This method reduced the complexity of the boundary caused by the additional new samples. The experimental results showed that the ClusteredSMOTE-Boost algorithm was superior to the three classical algorithms SMOTE-Boost, ADASYN-Boost, BorderlineSMOTE-Boost on the variety of measures
| [1] | HE H, GARCIA E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge & Data Engineering, 2009, 21(9):1263-1284. |
| [2] | KUMAR M, BHUTANI K, AGGARWAL S. Hybrid model for medical diagnosis using neutrosophic cognitive maps with genetic algorithms[C] //IEEE International Conference on Fuzzy Systems. Istanbul, Turkey: IEEE, 2015:1-7. |
| [3] | SRIVASTAVA A, KUNDU A, SURAL S, et al. Credit card fraud detection using hidden Markov model[J]. IEEE Transactions on Dependable & Secure Computing, 2008, 5(1):37-48. |
| [4] | SAEZ J A, LUENGO J, STEFANWSKI J, et al. SMOTE—IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering[J]. Information Sciences, 2015, 291(5):184-203. |
| [5] | RAMENTOL E, CABALLERO Y, BELLO R, et al. SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and under sampling for high imbalanced data-sets using SMOTE and rough sets theory[J]. Knowledge & Information Systems, 2012, 33(2):245-265. |
| [6] | BARUA S, ISLAM M M, YAO X, et al. MWMOTE: majority weighted minority over sampling technique for imbalanced data set learning[J]. IEEE Transactions on Knowledge & Data Engineering, 2014, 26(2):405-425. |
| [7] | ZENG H J, HE Q C, CHEN Z, et al. Learning to cluster web search results[C] //International ACM SIGIR Conference on Research and Development in Information Retrieval. Sheffield, UK: ACM, 2004:210-217. |
| [8] | HE H, BAI Y, GARCIA E A, et al. ADASYN: adaptive synthetic sampling approach for imbalanced learning[C] //IEEE International Joint Conference on Neural Networks. Hoboken, USA: IEEE, 2008:1322-1328. |
| [9] | FOMIN S, GRIGORIEV D, KOSHEVOY G. Subtraction-free complexity, cluster transformations, and spanning trees[J]. Foundations of Computational Mathematics, 2016, 16(1):1-31. |
| [10] | DAVIES D L, BOULDIN D W. A cluster separation measure[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1979, 1(2):224. |
| [11] | BORAL A, CYGAN M, KOCIUMAKA T, et al. A fast branching algorithm for cluster vertex deletion[J]. Theory of Computing Systems, 2016, 58(2):357-376. |
| [12] | BUNKHUMPORNPAT C, SINAPIROMSARAN K, LURSINSAP C. Safe-Level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem[C] //Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin, Germany: Springer, 2009:475-482. |
| [13] | LI J, FONG S, MOHAMMED S, et al. Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms[J]. Journal of Supercomputing, 2016, 72(10): 3708-3728. |
| [14] | WANG S, YAO X. Multi-class imbalance problems: analysis and potential solutions[J]. IEEE Transactions on Systems Man & Cybernetics: Part B, 2012, 42(4):1119-1130. |
| [15] | 杨明, 尹军梅, 吉根林. 不平衡数据分类方法综述[J]. 南京师范大学学报(工程技术版), 2008, 8(4): 7-12. YANG Ming, YIN Junmei, JI Genlin. Classification methods on imbalanced data: a survey[J]. Journal of Nanjing Normal University(Engineering and Technology Edition), 2008, 8(4): 7-12. |
| [16] | SUN Z, SONG Q, ZHU X, et al. A novel ensemble method for classifying imbalanced data[J]. Pattern Recognition, 2015, 48(5):1623-1637. |
| [17] | 胡小生, 张润晶, 钟勇. 一种基于聚类提升的不平衡数据分类算法[J]. 集成技术, 2014(2):35-41. HU Xiaosheng, ZHANG Runjing, ZHONG Yong. A clustering-based enhanced classification algorithm for imbalanced data[J]. Journal of Integration Technology, 2014(2):35-41. |
| [18] | CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2011, 16(1):321-357. |
| [19] | HAN H, WANG W Y, MAO B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[J]. Lecture Notes in Computer Science, 2005, 3644(5):878-887. |
| [20] | CHAWLA N V, LAZAREVIC A, HALL L O, et al. SMOTEBoost: improving prediction of the minority class in boosting[J]. Lecture Notes in Computer Science, 2003, 2838:107-119. |
