基于混淆矩阵和Fisher准则构造层次化分类器

Construction of Hierarchical Classifiers Based on the Confusion Matrix and Fisher’s Principle

构造层次化分类器的首要环节是确定各个子分类器的层属关系及其内部组成.从模式间的相似关系入手,实现了一种自动产生层次化分类器结构的方法.为了描述模式间的相似关系,首先提出利用混淆矩阵度量相似性的思路与方法,避免了现有常用度量方法计算量大、假设条件难以成立的不足.进而遵循Fisher准则,设计并实现了模式相似关系分析机(patterns’similarityrelationshipanalyzingmachine,简称PSRAM),将有师指派和无师自组两种常用的模式重组方法有机结合起来,自适应地产生层次化分类器结构.大量测试证实,该方法有效、实用,可以显著地提高分类器的识别性能和稳健性.

Determination of the hierarchical relationship and the objective patterns of sub-classifiers is a primary problem in the construction of a hierarchical classifier. In this paper, a method focusing on the similarities between patterns is proposed to generate a hierarchical structure automatically. Firstly, a similarity measurement utilizing the confusion matrix is advanced to avoid the drawbacks of the traditional measurements, such as high computation costs and invalidity of preliminary conditions. Then abiding by Fisher＇s Principle, a Patterns＇ Similarity Relationship Analyzing Machine （PSRAM）, which is integrated with the supervised and unsupervised pattern recombination methods, is designed to adaptively construct the structure of a hierarchical classifier. Various tests are testified that the proposed method is effective and practical, and it can prominently improve the performance and robustness of the hierarchical classifier.