Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3032-3038.DOI: 10.11772/j.issn.1001-9081.2023101422

• Artificial intelligence • Previous Articles     Next Articles

Robust weight matrix combination selection method of broad learning system

Han WANG1, Yuan WAN1, Dong WANG1, Yiming DING2()   

  1. 1.School of Science,Wuhan University of Technology,Wuhan Hubei 430070,China
    2.School of Sciences,Wuhan University of Science and Technology,Wuhan Hubei 430081,China
  • Received:2023-10-24 Revised:2024-03-04 Accepted:2024-04-03 Online:2024-10-15 Published:2024-10-10
  • Contact: Yiming DING
  • About author:WANG Han, born in 2000, M. S. candidate. Her research interests include broad learning system, machine learning.
    WAN Yuan, born in 1976, Ph. D., professor. Her research interests include artificial intelligence, machine learning, data mining.
    WANG Dong, born in 1998, M. S. candidate. His research interests include neural network, data analysis.
  • Supported by:
    National Key Research and Development Program of China(2020YFA0714200)

宽度学习系统中鲁棒性权值矩阵组合的筛选方法

汪韩1, 万源1, 王东1, 丁义明2()   

  1. 1.武汉理工大学 理学院,武汉 430070
    2.武汉科技大学 理学院,武汉 430081
  • 通讯作者: 丁义明
  • 作者简介:汪韩(2000—),女,湖北孝感人,硕士研究生,主要研究方向:宽度学习系统、机器学习
    万源(1976—),女,湖北武汉人,教授,博士,CCF会员,主要研究方向:人工智能、机器学习、数据挖掘
    王东(1998—),男,河南南阳人,硕士研究生,主要研究方向:神经网络、数据分析
    丁义明(1972—),男,江西丰城人,教授,博士,主要研究方向:动力系统、统计学习 dingym@wust.edu.cn
  • 基金资助:
    国家重点研发计划项目(2020YFA0714200)

Abstract:

Broad Learning System (BLS) has excellent computational efficiency and prediction accuracy. However, in the traditional BLS framework, the weight matrix is randomly generated with the risk of unstable learning results. Therefore, a Robust Weight matrix Selection of BLS(RWS-BLS) method was proposed. Firstly, the significant difference of the randomized weight matrix in the overall training error of the samples was revealed by the validation of four sets of function data. Secondly, the combination forms of the weight matrices were studied, the strict optimal restriction of the screening conditions was relaxed, the optimal restriction was converted into the better restriction, the minimum value of the error was limited in a specified range, and the conditions such as the elite combinations were defined. Finally, the combinations of the reliable weight matrices were obtained, so that the influence of randomness was effectively reduced and a robust model could be established. The experimental results show that on 16 sets of simulated data, NORB dataset and 5 sets of UCI regression dataset, with data replacement or subjected to noise perturbation, the proposed method decreases the Mean Square Error (MSE) by 7.32%, 8.73% and 1.63% compared with the BLS method. RWS-BLS provides a direction for model smoothness study for BLS, which can improve the efficiency and stability of models with stochastic parameters, and is useful for other machine learning methods with stochastic parameters.

Key words: Broad Learning System (BLS), weight matrix combination, feature node, enhancement node, robustness analysis

摘要:

宽度学习系统(BLS)具有出色的计算效率和预测准确性;然而,在传统BLS框架中,权值矩阵采用随机生成的方式,存在学习结果不稳定的风险。因此,设计一种BLS中鲁棒性权值矩阵组合的筛选方法(RWS-BLS)。首先,通过4组函数数据的验证,揭示随机权值矩阵在样本整体训练误差上的显著差异性;其次,研究权值矩阵组合的形式,放宽筛选条件的严格最优限制,将最优转换为较优,并将误差最小值限定在指定范围内,定义精英组合等条件;最后,得到可靠的权值矩阵的组合,有效降低随机性影响,并建立稳健的模型。实验结果表明,在16组模拟数据、NORB数据集和5组UCI回归数据集上,在数据更换或受噪声扰动的情况下,与BLS方法相比,所提方法的均方误差(MSE)下降了7.32%、8.73%和1.63%。RWS-BLS为BLS提供了一种模型平稳性研究的方向,提高了含有随机参数模型的效率和稳定性,并对涉及随机参数的其他机器学习方法具有借鉴作用。

关键词: 宽度学习系统, 权值矩阵组合, 特征节点, 增强节点, 鲁棒性分析

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