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山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 22-28.doi: 10.6040/j.issn.1672-3961.1.2015.046

• 机器学习与数据挖掘 • 上一篇    下一篇

基于FCA与CBR的设计模式检测

肖卓宇1,何锫2,余波1,黎妍3,胡振涛1   

  1. 1. 中南林业科技大学涉外学院, 湖南 长沙 410200;2. 广州大学计算机科学与教育软件学院, 广东 广州 510006;3. 湖南高速公路管理局, 湖南 长沙 410209
  • 收稿日期:2015-05-12 出版日期:2016-04-20 发布日期:2015-05-12
  • 作者简介:肖卓宇(1979— ),男,湖南长沙人,副教授,高级工程师,硕士,主要研究方向为程序理解,逆向工程与数据挖掘. E-mail: xzyxzy0770@126.com
  • 基金资助:
    国家自然科学基金资助项目(61170199);湖南省教育厅重点基金资助项目(11A004);湖南省大学生研究性学习和创新性实验计划资助项目(湘教通［2015］84号197);广东省自然科学基金资助项目(2015A030313501);中南林业科技大学教学改革资助项目(201509,201611)

Design patterns detection based on FCA and CBR

XIAO Zhuoyu1, HE Pei2, YU Bo1, LI Yan3, HU Zhentao1   

  1. 1. Swan College, Central South University of Forestry and Technology, Changsha 410200, Hunan, China;
    2. School of Computer Science &
    Education, Guangzhou University, Guangzhou 510006, Guangdong, China;
    3. Hunan Highway Administration Bureau, Changsha 410209, Hunan, China
  • Received:2015-05-12 Online:2016-04-20 Published:2015-05-12

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摘要： 以一个精确可靠的设计模式检测模型为目标,结合形式概念分析(formal concept analysis, FCA)与实例推理(case based reasoning, CBR)技术,提出一种基于更完整问题描述的改进技术模型,通过FCA与余弦理论思想计算特征指标与相近案例的相似性值Score,对其结果进行优先级排序,并取得最优选择之后,将其匹配的特征结果存储到学习模型的保存过程阶段。最后,给出一种基于平均精度MAP的性能评估方法模型。试验结果表明,该检测模型较传统的检测模型在性能上有较大改进。

关键词: 实例推理, 平均精度, 概念格, 典型特征值, 设计模式检测, 形式概念分析

Abstract: Aiming to obtain the accurate and reliable detecting model of design patterns that fusion formal concept analysis(FCA)techniques and case cased reasoning(CBR), a novel refinement technique based on more complete software problem description was proposed. Indexes and cases similarity score value was calculated by FCA and Cosine theory. The results of the priority achieved optimal choices, the new knowledge for the retention process phase of the learning model was provided. An approach based on mean average precision(MAP)to assess the performance was proposed. Finally, the experimental results showed that the presented model had more detecting ability in term of MAP comparing to the traditional models.

Key words: case based reasoning, mean average precision, concept lattice, formal concept analysis, design pattern detection, typical feature value

中图分类号: 

  • TP311
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