Page 78 - 《应用声学》2021年第5期
P. 78

722                                                                                  2021 年 9 月


             于最小可检测阈6 dB 的数据上,未来将进一步研究                             IEEE 36th International Conference on Data Engineer-
             该算法在更低可检测信噪比数据集上的不平衡分                                 ing. IEEE Computer Society, 2020: 841–851.
                                                                 [7] Han J, Kamber M, Pei J. 数据挖掘: 概念与技术 [M]. 范明,
             类效果。                                                  孟小峰, 译. 第三版. 北京: 机械工业出版社, 2019: 250–251.
                                                                 [8] Núez H, Gonzalez-Abril L, Angulo C. Improving SVM
                                                                   classification on imbalanced datasets by introducing a new
                            参 考     文   献                          bias[J]. Journal of Classification, 2017, 34(3): 427–443.
                                                                 [9] Vanhoeyveld J, Martens D. Imbalanced classification in
                                                                   sparse and large behaviour datasets[J]. Data Mining &
              [1] Berg H, Hjelmervik K T. Classification of anti-submarine  Knowledge Discovery, 2018, 32(1): 1–58.
                 warfare sonar targets using a deep neural network[C].  [10] Lin Y, Yoonkyung L, Grace W. Support vector machines
                 OCEANS Marine Technology Society. IEEE Charleston,  for classification in nonstandard situations[J]. Machine
                 2018: 1–5.                                        Learning, 2002, 46(1/2/3): 191–202.
              [2] Berg H, Hjelmervik K T. A comparison of different ma-  [11] Zheng E, Li P, Song Z. Cost sensitive support vector ma-
                 chine learning algorithms for automatic classification of  chines[J]. Control & Decision, 2006, 21(4): 473–476.
                 sonar targets[C]. OCEANS Marine Technology Society.  [12] Liu N, Qi E, Xu M, et al. A novel intelligent classifi-
                 IEEE Monterey, 2016: 1–8.                         cation model for breast cancer diagnosis[J]. Information
              [3] Stender D H, Hjelmervik K T, Berg H, et al. Sensitivity  Processing & Management, 2019, 56(3): 609–623.
                 to target behavior in automatic classification on kinematic  [13] 于化龙. 类别不平衡学习: 理论与算法 [M]. 北京: 清华大学
                 track features[C]. OCEANS Marine Technology Society.  出版社, 2017: 4–5.
                 IEEE Kobe, 2018: 1–5.                          [14] Zhang T. Statistical behavior and consistency of classifi-
              [4] Stender D H, Hjelmervik K T, Berg H, et al. The classifi-  cation methods based on convex risk minimization[J]. The
                 cation performance of signal-to-noise ratio and kinematic  Annals of Statistics, 2004, 32(1): 56–85.
                 features in varying environments[C]. OCEANS. IEEE Ab-  [15] Lin H T, Lin C J, Weng R C. A note on Platt’s proba-
                 erdeen, 2017: 1–5.                                bilistic outputs for support vector machines[J]. Machine
              [5] 赵楠, 张小芳, 张利军. 不平衡数据分类研究综述 [J]. 计算机               Learning, 2007, 68(3): 267–276.
                 科学, 2018, 45(6A): 22–27.                       [16] Székely G J, Rizzo M L. Energy statistics: a class of statis-
              [6] Liu Z, Cao W, Gao Z, et al.  Self-paced ensemble  tics based on distances[J]. Journal of Statistical Planning
                 for Highly imbalanced massive data classification[C].  and Inference, 2013, 143(8): 1249–1272.
   73   74   75   76   77   78   79   80   81   82   83