基于KNN模型的层次纠错输出编码算法

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。

Output code algorithm for ierarchical error correcting based on KNNModel

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.