Rough set-based heuristic hybrid recognizer and its application in fault diagnosis
Rough set-based heuristic hybrid recognizer and its application in fault diagnosis
复制标题
基于粗糙集的启发式混合识别器及其在故障诊断中的应用
DOI：
10.1016/j.eswa.2008.01.020
复制
发表时间：
2009-03-01
影响因子：
8.5
通讯作者：
Zhu, Qunxiong
中科院分区：
文献类型：
--
作者：
Geng, Zhiqiang;Zhu, Qunxiong
粗糙集理论是知识发现和模式识别领域的一个研究热点。众所周知，机器学习算法在面对许多规则发现不必要的特征（有时是属性）时会降低性能。已经提出了许多用于选择特征子集的方法。针对单一方法难以处理具有多属性或多特征的复杂系统的问题，提出了一种基于粗糙集和人工神经网络（Rough-ANN）的混合模式识别特征选择方法。基于粗糙集的属性约简作为神经网络的预处理器，可以减少神经网络的输入，提高训练速度。从而避免了粗糙集对噪声的敏感性，提高了系统的鲁棒性。提出了一种基于粗糙集的启发式特征选择算法。该方法可以从大量的特征数据库中快速有效地选择出最优的特征子集。通过工业过程的实际实验和故障诊断，验证了所提出的混合识别器和解决方案的有效性。(C)2008年由Elsevier Ltd.出版
Rough set theory (RS) has been a topic of general interest in the field of knowledge discovery and pattern recognition. Machine learning algorithms arc known to degrade in performance when faced with many features (sometimes attributes) that are not necessary for rule discovery. Many methods for selecting a subset of features have been proposed. However, only one method cannot handle the complex system with many attributes or features, so a hybrid mechanism is proposed based oil rough set integrating artificial neural network (Rough-ANN) for feature selection in pattern recognition. RS-based attributes reduction as the preprocessor can decrease the inputs of the NN and improve the speed of training. So the sensitivity of rough set to noise can be avoided and the system's robustness is to be improved. A RS-based heuristic algorithm is proposed for feature selection. The approach can select ail optimal subset of features quickly and effectively from a large database with a lot of features. Moreover, the validity of the proposed hybrid recognizer and solution is verified by the application of practical experiments and fault diagnosis in industrial process. (C) 2008 Published by Elsevier Ltd.