基于社交网络的双知识表达分类方法
作者:
作者单位:

(1. 江南大学数字媒体学院,江苏无锡214122; 2. 常州轻工职业技术学院信息工程与技术学院,江苏常州213164)

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E-mail: gusuhang09@163.com.

中图分类号:

TP391

基金项目:

国家自然科学基金项目(61572236,61300151);常州工业职业技术学院博士基金项目(BSJJ13101010);常州工业职业技术学院新一代信息技术团队项目(YB201813101005);常州市科技计划项目(CJ20190016).


Double knowledge representations based classification method from perspective of social networks
Author:
Affiliation:

(1. School of Digital Media,Jiangnan University,Wuxi 214122,China;2. School of Information Engineering and Technology,Changzhou Institute of Industry Technology,Changzhou 213164,China)

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    摘要:

    针对实际数据集中的每一类数据都潜在或显著地包含独有的数据风格信息,提出一种挖掘数据风格信息的双知识表达分类方法.在训练阶段,利用K近邻(KNN)算法构建社交网络以表达数据点之间的组织架构,并利用社交网络属性挖掘数据点及每一类数据整体风格信息.在分类阶段,用双知识表达约束所提出方法的分类行为,即赋予测试样本标签时既要使该样本物理上与所建分类模型最相似,也要使该样本风格上与分类模型最相似.与其他对比分类方法相比,所提出方法在不包含或包含不显著风格的数据集上至少能够取得竞争性的分类性能,在包含明显风格的数据集上能够取得优越性的分类性能.

    Abstract:

    Since the distinguished style information of data may latently or obviously present in each data class in a given real-world dataset, a double knowledge representations based classification method (DKR-CM) from the perspective of social networks is proposed. In the training stage, a social network corresponding to all data samples in a dataset is easily built using the on-hand KNN method. In addition, style information of each data sample and each data class are respectively exploited in the social network. In the prediction stage, the proposed double knowledge representations (DKR) is utilized to improve the classification hebaviors of the DKR-SCM. In other words, each data sample is classified into the data class which it approaches to as far as possible from the perspectives of both physical features and style information of data. Experimental results demonstrate that the DKR-CM is at least comparative to the compared classification methods on the datasets with no or inapparent style information and outperforms them on the datasets with obvious style information.

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引用本文

顾苏杭,王士同.基于社交网络的双知识表达分类方法[J].控制与决策,2020,35(11):2653-2664

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  • 在线发布日期: 2020-10-15
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