[1]左鹏玉,周洁,王士同.面对类别不平衡的增量在线序列极限学习机[J].智能系统学报,2020,15(3):520-527.[doi:10.11992/tis.201904040]
 ZUO Pengyu,ZHOU Jie,WANG Shitong.Incremental online sequential extreme learning machine for imbalanced data[J].CAAI Transactions on Intelligent Systems,2020,15(3):520-527.[doi:10.11992/tis.201904040]
点击复制

面对类别不平衡的增量在线序列极限学习机(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年3期
页码:
520-527
栏目:
学术论文—人工智能基础
出版日期:
2020-05-05

文章信息/Info

Title:
Incremental online sequential extreme learning machine for imbalanced data
作者:
左鹏玉1 周洁1 王士同12
1. 江南大学 数字媒体学院,江苏 无锡 214122;
2. 江苏省媒体设计与软件设计重点实验室,江苏 无锡 214122
Author(s):
ZUO Pengyu1 ZHOU Jie1 WANG Shitong12
1. College of Digital Media, Jiangnan University, Wuxi 214122, China;
2. Jiangsu Province Key Lab. of Media Design & Software Technologies, Wuxi 214122, China
关键词:
类别不平衡学习增量无逆矩阵在线学习极限学习机分类多类不平衡神经网络
Keywords:
class imbalanceincremental learninginverse-free matrixonline learningextreme learning machineclassificationmulti-class imbalancedneural network
分类号:
TP181
DOI:
10.11992/tis.201904040
摘要:
针对在线序列极限学习机对于类别不平衡数据的学习效率低、分类准确率差的问题,提出了面对类别不平衡的增量在线序列极限学习机(IOS-ELM)。该算法根据类别不平衡比例调整平衡因子,利用分块矩阵的广义逆矩阵对隐含层节点数进行寻优,提高了模型对类别不平衡数据的在线处理能力,最后通过14个二类和多类不平衡数据集对该算法有效性和可行性进行验证。实验结果表明:该算法与同类其他算法相比具有更好的泛化性和准确率,适用于类别不平衡场景下的在线学习。
Abstract:
In this paper, an incremental online sequential extreme learning machine (IOS-ELM) is proposed to solve the problems of low efficiency and poor classification accuracy of OS-ELM for class imbalance learning. The basic idea is to adjust the balance factor according to the category imbalance ratio in an imbalanced dataset and then determine an optimal number of hidden nodes using the generalized inverse of the block matrix, thereby improving the online learning ability of IOS-ELM. The experiments on the effectiveness and feasibility of 14 binary-class and multi-class imbalanced datasets show that the proposed IOS-ELM has better generalization capability and classification performance than other comparative methods.

参考文献/References:

[1] HUANG Guangbin, ZHU Qinyu, SIEW C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[2] HUANG Guangbin, CHEN Lei, SIEW C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes[J]. IEEE transactions on neural networks, 2006, 17(4): 879-892.
[3] LI Shuai, YOU Zhuhong, GUO Hongliang, et al. Inverse-free extreme learning machine with optimal information updating[J]. IEEE transactions on cybernetics, 2016, 46(5): 1229-1241.
[4] HUANG Shan, WANG Botao, CHEN Yuemei, et al. An efficient parallel method for batched OS-ELM training using MapReduce[J]. Memetic computing, 2017, 9(3): 183-197.
[5] KIM Y, TOH K A, TEOH A B J, et al. An online learning network for biometric scores fusion[J]. Neurocomputing, 2013, 102: 65-77.
[6] LIANG Nanying, HUANG Guangbin, SARATCHANDRAN P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE transactions on neural networks, 2006, 17(6): 1411-1423.
[7] 张明洋, 闻英友, 杨晓陶, 等. 一种基于增量加权平均的在线序贯极限学习机算法[J]. 控制与决策, 2017, 32(10): 1887-1893
ZHANG Mingyang, WEN Yingyou, YANG Xiaotao, et al. An incremental weighted average based online sequential extreme learning machine algorithm[J]. Control and decision, 2017, 32(10): 1887-1893
[8] DOUZAS G, BACAO F, LAST F. Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE[J]. Information sciences, 2018, 465: 1-20.
[9] BATUWITA R, PALADE V. Class imbalance learning methods for support vector machines[M]//HE Haibo, MA Yunqian. Imbalanced Learning: Foundations, Algorithms, and Applications. New York: John Wiley & Sons, Inc., 2013: 145-168.
[10] XIA Shixiong, MENG Fanrong, LIU Bing, et al. A Kernel Clustering-based possibilistic fuzzy extreme learning machine for class imbalance learning[J]. Cognitive computation, 2015, 7(1): 74-85.
[11] ZONG Weiwei, HUANG Guangbin, CHEN Yiqiang. Weighted extreme learning machine for imbalance learning[J]. Neurocomputing, 2013, 101: 229-242.
[12] MIRZA B, LIN Zhiping, TOH K A. Weighted online sequential extreme learning machine for class imbalance learning[J]. Neural processing letters, 2013, 38(3): 465-486.
[13] HUANG Guangbin, ZHOU Hongming, DING Xiaojian, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 2012, 42(2): 513-529.
[14] RAO C R, MITRA S K. Generalized inverse of a matrix and its applications[C]//Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Theory of Statistics. Berkeley, University of California Press, 1972: 601-620.
[15] BATUWITA R, PALADE V. FSVM-CIL: fuzzy support vector machines for class imbalance learning[J]. IEEE transactions on fuzzy systems, 2010, 18(3): 558-571.
[16] DING Shuya, MIRZA B, LIN Zhiping, et al. Kernel based online learning for imbalance multiclass classification[J]. Neurocomputing, 2017, 277: 139-148.
[17] HE H, GARCIA E A. Learning from imbalance data[J]. IEEE transactions on knowledge and data engineering, 2009, 21(9): 1263-1284.

备注/Memo

备注/Memo:
收稿日期:2019-04-17。
基金项目:国家自然科学基金项目(61170122)
作者简介:左鹏玉,硕士研究生,主要研究方向为人工智能、模式识别;周洁,博士研究生,主要研究方向为人工智能、模式识别、机器学习;王士同,教授,博士生导师,CCF会员,主要研究方向为人工智能、模式识别。作为第一作者发表学术论文百余篇
通讯作者:左鹏玉.E-mail:1253712018@qq.com
更新日期/Last Update: 1900-01-01