[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]
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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]
面对类别不平衡的增量在线序列极限学习机
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第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
- Title:
- Incremental online sequential extreme learning machine for imbalanced data
- Keywords:
- class imbalance; incremental learning; inverse-free matrix; online learning; extreme learning machine; classification; multi-class imbalanced; neural network
- 分类号:
- TP181
- 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:
-
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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