Performance enhancement of extreme learning machine for multi-category sparse data classification problems
Performance enhancement of extreme learning machine for multi-category sparse data classification problems
复制标题
DOI：
10.1016/j.engappai.2010.06.009
复制
发表时间：
2010-10-01
影响因子：
8
通讯作者：
Sundararajan, N.
中科院分区：
文献类型：
--
作者：
Suresh, S.;Saraswathi, S.;Sundararajan, N.
本文为最近开发的极限学习机（ELM）提供了一种针对多类稀疏数据分类问题的性能增强方案。 ELM是一个具有良好概括能力和非常快速学习能力的单一隐藏层神经网络。在ELM中，输入权重是随机选择的，并通过分析计算输出权重。稀疏数据分类问题的ELM算法的概括性能取决于三个免费参数。它们是隐藏神经元的数量，输入权重和需要最佳选择的偏置值。选择这些参数以获得ELM的最佳性能涉及一个复杂的优化问题。在本文中，我们提出了一种称为“ rcga-elm”的新的，真实编码的遗传算法方法，以选择最佳数量的隐藏神经元，输入权重和偏见值会带来更好的性能。提出了两个称为“基于网络的操作员”和“基于权重的操作员”的新遗传运营商，以找到具有更高概括性能的紧凑网络。我们还提出了一种称为“稀疏ELM”的替代和计算密集的方法。稀疏ELM使用K折验证搜索ELM的最佳参数。使用微阵列基因表达数据（稀疏）的多级人类癌症分类问题，用于评估两个方案的性能。结果表明，提出的RCGA-ELM和稀疏ELM显着改善了稀疏多类别分类问题的ELM性能。 （c）2010 Elsevier Ltd.保留所有权利。
This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for multi-category sparse data classification problems. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for sparse data classification problem depends critically on three free parameters. They are, the number of hidden neurons, the input weights and the bias values which need to be optimally chosen. Selection of these parameters for the best performance of ELM involves a complex optimization problem.In this paper, we present a new, real-coded genetic algorithm approach called 'RCGA-ELM' to select the optimal number of hidden neurons, input weights and bias values which results in better performance. Two new genetic operators called 'network based operator' and 'weight based operator' are proposed to find a compact network with higher generalization performance. We also present an alternate and less computationally intensive approach called 'sparse-ELM'. Sparse-ELM searches for the best parameters of ELM using K-fold validation. A multi-class human cancer classification problem using micro-array gene expression data (which is sparse), is used for evaluating the performance of the two schemes. Results indicate that the proposed RCGA-ELM and sparse-ELM significantly improve ELM performance for sparse multi-category classification problems. (C) 2010 Elsevier Ltd. All rights reserved.