基于神经网络的纠错输出编码方法研究

构造基于数据编码矩阵是目前利用纠错输出编码解决多类分类问题的研究重点.为此提出利用单层感知器作为学习框架,结合解码策略把输出编码矩阵各码元值映射为感知器网络中的权值,同时引入含权值取值约束的目标函数作为该网络代价函数,并对其进行学习,最终得到基于子类划分的数据编码矩阵.实验中利用人工数据集和UCI数据集并选择线性逻辑分类器作为基分类器分别进行测试,通过与几种经典编码方法比较,结果表明该编码方法能在编码长度较小情况下得到更好的分类效果.

Coding Design for Error Correcting Output Codes Based on Neural Network

It is known that error-correcting output codes (ECOC) is a common way to model multiclass classification problems,in which the research of encoding based on data especially attracts attentions.In this paper,we proposed a method for learning error-correcting output codes with the help of a single layered perception neural network.To achieve this goal,the code elements of ECOC are mapped to the weights of network for the given decoding strategy,and an object function with the constrained weights used as a cost function of network.After the training,we can obtain a coding matrix including lots of subgroups of class.Experimental results on artificial data and UCI with logistic linear classifier (LOGLC) as the binary learner show that our scheme provides better performance of classification with shorter length of coding matrix than other state-of-the-art encoding strategies.