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.

Key words: multi-class classification, one-versus-one(OVO) decomposition, aggregation strategy, cigarette sensory quality, intelligent evaluation