基于改良蚁群算法的神经网络分类规则提取

Rules Extraction from Artificial Neural Networks for Classification Based Improved Ant Colony Algorithm

在数据挖掘领域,分类获得了很大的关注度,其主要目的是预测数据对象的所属类别。分类方法可分为基于规则和不基于规则两大类,其中神经网络由于在预测、从经验中学习、从先前样本中泛化等方面的优秀表现,使其成为分类领域的一个重要的方法,并往往能够获得很高的分类准确性,然而其非常有限的解释能力成为了制约其应用的一大缺陷。提出了一种基于改良蚁群算法的神经网络分类规则提取方法,通过改良的蚁群算法来填补神经网络有限的解释能力,从数据中提取出分类规则。实验证明,该方法能够很好的辅助神经网络,从要分类的数据中获取规则。

Classification obtains great concern in the field of data mining.Its main purpose is to predict the classification of data objects.Classification can be divided into two major categories of rule-based and non-rule-based,however because of the excellent performance that artificial neural network（ANN） can obtain from prediction,studying from experience and generalizing from the previous samples,making it an important method of classification.Although ANNs can achieve high classification accuracy,their explanation capability is very limited,as to restrict its application.This paper presents an improved ant colony algorithm based on ANNs classification rule extraction method,an improved ant colony algorithm is to help solve the ANN＇s limited explanation capability to extract rules from the data.Experiments show that this approach could coordinate neural network to obtain rules of classified data well.