Case-Based Classification on Hierarchical Structure of Formal Concept Analysis

被引：0

作者：

Zhang, Qi ^{[1
]}

Shi, Chongyang ^{[1
]}

Sun, Ping ^{[1
]}

Niu, Zhengdong ^{[1
]}

机构：

[1] Beijing Inst Technol, Beijing, Peoples R China

来源：

ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年 / 285卷

关键词：

D O I：

10.3233/978-1-61499-672-9-1758

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a novel Hierarchical CBC model (HCBC) based on Formal Concept Analysis (FCA). Firstly, Concept Lattice (CL), the hierarchical and conceptual structure in FCA, is adopted to represent cases. Thus a novel dynamic weight model is proposed from CL to measure similarities between cases and concepts. Then the similarity metric is applied to retrieve the top-K similar concepts which are used to vote for adaptive solutions for new cases by majority voting in case adaption. Experiments show our model shows good performance in terms of accuracy and outperforms the other classification methods.

引用

页码：1758 / 1759

页数：2

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