A multiobjective optimization-based sparse extreme learning machine algorithm
A multiobjective optimization-based sparse extreme learning machine algorithm
复制标题
一种基于多目标优化的稀疏极限学习机算法
DOI：
10.1016/j.neucom.2018.07.060
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发表时间：
2018
期刊：
影响因子：
6
通讯作者：
Cai Yaoming
中科院分区：
文献类型：
--
作者：
Wu Yu;Zhang Yongshan;Liu Xiaobo;Cai Zhihua;Cai Yaoming
极限学习机（ELM）是一种流行的机器学习方法，由于其快速的训练速度和良好的泛化性能，已被广泛应用于现实世界的问题。然而，在ELM中，随机分配的输入权重和隐藏的偏见通常会降低泛化性能。此外，ELM被认为是一个经验风险最小化模型，当数据集存在一些离群值时，容易导致过拟合。本文提出了一种基于多目标优化的稀疏极限学习机（MO-SELM）算法，将参数优化和结构学习结合到学习过程中，以提高泛化性能和缓解过拟合问题。MO-SELM将训练误差和连接稀疏性作为多目标模型的两个相互冲突的目标，旨在寻找具有最优权值和偏置的稀疏连接结构。然后，基于混合编码的MOEA/D被用来优化多目标模型。此外，集成学习嵌入到该算法进行决策后，多目标优化。几个分类和回归应用程序的实验结果表明，所提出的MO-SELM的有效性。
Extreme Learning Machine (ELM) is a popular machine learning method and has been widely applied to real-world problems due to its fast training speed and good generalization performance. However, in ELM, the randomly assigned input weights and hidden biases usually degrade the generalization performance. Furthermore, ELM is considered as an empirical risk minimization model and easily leads to overfitting when dataset exists some outliers. In this paper, we proposed a novel algorithm named Multiobjective Optimization-based Sparse Extreme Learning Machine (MO-SELM), where parameter optimization and structure learning are integrated into the learning process to simultaneously enhance the generalization performance and alleviate the overfitting problem. In MO-SELM, the training error and the connecting sparsity are taken as two conflicting objectives of the multiobjective model, which aims to find sparse connecting structures with optimal weights and biases. Then, a hybrid encoding-based MOEA/D is used to optimize the multiobjective model. In addition, ensemble learning is embedded into this algorithm to make decisions after multiobjective optimization. Experimental results of several classification and regression applications demonstrate the effectiveness of the proposed MO-SELM.