基于OVO分解策略的智能卷烟感官评估方法

摘要 针对智能卷烟感官评估系统中涉及的多分类问题，采用“一对一”(one-versus-one， OVO)分解策略将复杂的多分类问题分解成多个易于处理的二分类子问题，然后针对这些子问题分别建立二值分类器，最后采用一定的聚合策略将二值分类器组合成多类分类器.此外，分别采用基于动态分类器选择和基于距离相对竞争力加权法对OVO中的冗余二值分类器进行处理，从而降低其对OVO系统的消极影响.为了验证所采用的方法在智能卷烟感官评估中的有效性，采用国内某烟草公司提供的数据集进行对比实验.实验结果表明，在智能卷烟感官评估中基于OVO分解策略的多分类方法比传统方法具有更优的分类性能.
Abstract：Intelligent cigarette sensory evaluation system involves multi-class classification problems. The one-versus-one (OVO) decomposition strategy was employed to divide the multi-class classification problem into several easier-to-solve binary sub-problems. Then binary classifiers were established for these sub-problems. Finally， an aggregation strategy was adopted to combine the binary classifiers to be a multi-class classifier. In addition， dynamic classifier selection for OVO strategy (DCS-OVO) and distance-based relative competence weighting for OVO strategy (DRCW-OVO) were used to reduce the negative effect of the non-competent classifiers. In order to verify the effectiveness of the employed method in intelligent cigarette sensory evaluation， the experimental comparison by using the dataset from a Chinese tobacco company was carried out. The results indicate that the OVO decomposition strategy outperforms the classical methodology in intelligent cigarette sensory evaluation.