• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Econometrics and Financial Management >A Multilayer Perceptron Artificial Neural Networks Based a Preprocessing and Hybrid Optimization Task for Data Mining and Classification
【24h】

A Multilayer Perceptron Artificial Neural Networks Based a Preprocessing and Hybrid Optimization Task for Data Mining and Classification

机译：基于预处理和混合优化任务的多层感知器人工神经网络的数据挖掘和分类

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Artificial neural networks (ANNs) optimization represent an attractive area that attract many researchers in different disciplines, this in the aim to improve the performance of this model. In literature, there is no fix theory that illustrates how to construct this non linear model. Thus, all proposed construction was based on empirical illustration. Multilayer perceptron (MLP) is one of the most used models in ANNs area. It was described as a good non linear approximator with a power ability to lean well non linear system, and most of research was limited to a 3 layers MLP, by describing that 3 layers are sufficient to have good approximation. In this context we are interested to this model construction for solving supervised classification tasks in data mining. This construction requires a preprocessing phase that seems to scribe be important for the final performance. This paper present a process of MLP construction based on two phases: a preparation phase and an optimization phase. The first one describes a process of data cleaning, discretization, normalization, expansion, reduction and features selection. The second phase aims to optimize the set of weights based on some combination of hybrid algorithms such back-propagation algorithm, a local search and different evolution. An empirical illustration will be done to in order to validate the proposed model. At the end, a comparison with others known classifiers will be done to justify the validity of the proposed model.
机译：人工神经网络（ANN）优化是一个吸引人的领域，吸引了许多不同学科的研究人员，目的是提高此模型的性能。在文献中，没有固定理论可以说明如何构建此非线性模型。因此，所有提议的构造都是基于经验的说明。多层感知器（MLP）是ANNs地区最常用的模型之一。它被描述为具有良好的非线性系统能力的良好非线性近似器，并且大多数研究仅限于3层MLP，方法是描述3层足以具有良好的近似性。在这种情况下，我们对用于解决数据挖掘中监督分类任务的模型构建感兴趣。这种构造需要预处理阶段，这似乎对最终性能至关重要。本文提出了一个基于两个阶段的MLP构建过程：准备阶段和优化阶段。第一个描述了数据清理，离散化，规范化，扩展，约简和功能选择的过程。第二阶段旨在基于混合算法（例如反向传播算法），局部搜索和不同演化的某种组合来优化权重集。为了验证所提出的模型，将进行经验说明。最后，将与其他已知分类器进行比较，以证明所提出模型的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号