东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (1): 15-20.DOI: 10.12068/j.issn.1005-3026.2018.01.004

• 信息与控制 • 上一篇    下一篇

基于OVO分解策略的智能卷烟感官评估方法

张忠良1,2, 雒兴刚1,2, 汤建国3, 唐加福1   

  1. (1. 东北大学 信息科学与工程学院, 辽宁 沈阳110819; 2. 杭州电子科技大学 管理学院, 浙江 杭州310018; 3. 云南中烟工业有限责任公司 技术中心, 云南 昆明650231)
  • 收稿日期:2016-07-11 修回日期:2016-07-11 出版日期:2018-01-15 发布日期:2018-01-31
  • 通讯作者: 张忠良
  • 作者简介:张忠良(1986-),男,浙江嘉兴人,东北大学博士研究生; 雒兴刚(1971-),男,新疆奇台人,东北大学教授,博士生导师; 唐加福(1965-),男,湖南东安人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(71771070).

Intelligent Cigarette Sensory Evaluation Method Based on OVO Decomposition Strategy

ZHANG Zhong-liang1,2, LUO Xing-gang1,2, TANG Jian-guo 3, TANG Jia-fu1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. School of Management, Hangzhou Dianzi University, Hangzhou 310018, China; 3. Technology Center, China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China.
  • Received:2016-07-11 Revised:2016-07-11 Online:2018-01-15 Published:2018-01-31
  • Contact: LUO Xing-gang
  • About author:-
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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.

关键词: 多分类, 一对一分解, 聚合策略, 卷烟感官质量, 智能评估

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

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