计算机应用 ›› 2009, Vol. 29 ›› Issue (11): 3051-3055.

• 模式识别 • 上一篇    下一篇

基于KNN模型的层次纠错输出编码算法

辛轶1,郭躬德2,陈黎飞3,黄杰2   

  1. 1. 福建师范大学数学与计算机科学学院(仓山校区)08级研究生
    2. 福建师范大学数学与计算机科学学院
    3.
  • 收稿日期:2009-05-11 修回日期:2009-07-01 出版日期:2009-11-01 发布日期:2009-11-26
  • 通讯作者: 辛轶
  • 基金资助:
    多分类器融合技术及其应用研究;多分类器融合策略研究

Output code algorithm for ierarchical error correcting based on KNNModel

Yi yiXIN,Gong-de GUO,Li-fei CHEN,Jie HUANG   

  • Received:2009-05-11 Revised:2009-07-01 Online:2009-11-01 Published:2009-11-26
  • Contact: Yi yiXIN

摘要: 纠错输出编码是一种解决多类分类问题的有效方法,但其编码矩阵只对类进行编码且都采用事先构造出来的统一形式,适应性较差。为此,提出一种新颖的层次纠错输出编码算法。该算法在训练阶段先通过KNN模型算法在数据集上构建多个同类簇,选取各类中最具代表性的簇形成层次编码矩阵,然后再根据编码矩阵进行单分类器训练。在测试阶段,该算法通过模型融合进一步发挥KNN模型和纠错输出编码各自的优点。在UCI公共数据集上的实验结果表明,新方法的性能优于KNN模型算法和纠错输出编码算法。

关键词: 层次编码, 多类分类问题, 编码矩阵

Abstract: Error Correcting Output Codes (ECOC) is an effective algorithm to handle multi-class problem; however, the ECOC coding is only on the class level and the ECOC matrix is pre-designed. A novel classification algorithm based on hierarchical ECOC was proposed. The algorithm first used KNNModel to build multiple clusters on a given dataset and chose few clusters for each class as representatives to construct a hieratical coding matrix in training phase, and then the matrix was used to train each single classifier. In testing phase, the proposed method makes the most of the merits of KNNModel and ECOC through models combination. Experimental results in the UCI data sets show the effectiveness of the proposed method.

Key words: hierarchical coding, multi-class classification, coding matrix