Knowledge structure construction and skill reduction methods based on multi-scale context

被引:4
|
作者
Zhou, Yinfeng [1 ]
Li, Jinjin [2 ,3 ]
Yang, Hailong [1 ]
Xu, Qingyuan [4 ,5 ]
Zhou, Yueli [2 ]
机构
[1] Shaanxi Normal Univ, Sch Math & Stat, Xian, Shaanxi, Peoples R China
[2] Minnan Normal Univ, Sch Math & Stat, Zhangzhou, Fujian, Peoples R China
[3] Minnan Normal Univ, Key Lab Granular Comp & Applicat Fujian, Zhangzhou, Fujian, Peoples R China
[4] Minnan Normal Univ, Sch Comp Sci, Zhangzhou, Fujian, Peoples R China
[5] Minnan Normal Univ, Key Lab Data Sci & Intelligence Applicat Fujian Pr, Zhangzhou, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
Skill function; knowledge structure; multi-scale context; competency model; minimal skill function; FORMAL CONCEPT ANALYSIS; SPACE; BUILD;
D O I
10.1080/0952813X.2023.2183266
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The conjunctive model of skill map reflects a way to solve the items in a knowledge domain. Currently, a skill map has been transformed into a formal context to construct a knowledge structure. In fact, the conjunctive model of skill map can be regarded as a special case of skill function. In this paper, we consider a way to solve the items in a knowledge domain as a scale. As a result, a skill function can be decomposed into multiple formal contexts, which correspond to a multi-scale context. Based on this, a method for constructing a knowledge structure delineated via competency model by skill function is proposed. In addition, a method for finding minimal skill function is proposed to reduce the complexity. Last but not least, the experimental analysis of five data sets on the UCI Repository shows that the improved method of constructing knowledge structure is effective and feasible and that it is necessary to perform skill reduction on skill function.
引用
收藏
页码:1923 / 1942
页数:20
相关论文
共 50 条
  • [21] Superconvergent techniques in multi-scale methods
    Chen, Peimin
    Allegretto, Walter
    Lin, Yanping
    INTERNATIONAL JOURNAL OF NUMERICAL ANALYSIS AND MODELING, 2008, 5 (02) : 239 - 254
  • [22] Multi-scale kernel methods for classification
    Kingsbury, N
    Tay, DBH
    Palaniswami, M
    2005 IEEE WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2005, : 43 - 48
  • [23] Knowledge construction during hybrid training. Towards a multi-scale starstatement approach
    Perrin, Nicolas
    Drakos, Artemis
    Martin, Gaelle
    Piot, David
    REVUE D ANTHROPOLOGIE DES CONNAISSANCES, 2024, 18 (01):
  • [24] A Review of Multi-scale Methods in Electromagnetics
    Christopoulos, C.
    2016 IEEE INTERNATIONAL WORKSHOP ON ELECTROMAGNETICS: APPLICATIONS AND STUDENT INNOVATION COMPETITION (IWEM), 2016,
  • [25] Multi-scale Context Intertwining for Semantic Segmentation
    Lin, Di
    Ji, Yuanfeng
    Lischinski, Dani
    Cohen-Or, Daniel
    Huang, Hui
    COMPUTER VISION - ECCV 2018, PT III, 2018, 11207 : 622 - 638
  • [26] Knowledge-based interpretation of satellite data by object-based and multi-scale image analysis in the context of nuclear verification
    Niemeyer, I
    Canty, MJ
    IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 2982 - 2984
  • [27] Construction Vehicle Detection Method Based on Multi-Scale Residual Network
    Liu, Liangshuai
    Chen, Ze
    She, Kai
    Ji, Yanpeng
    Feng, Haiyan
    Ni, Yong
    2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 1399 - 1405
  • [28] Transforming construction: the multi-scale challenges of changing and innovating in construction
    Glass, Jacqueline
    Bygballe, Lena E.
    Hall, Daniel
    CONSTRUCTION MANAGEMENT AND ECONOMICS, 2022, 40 (11-12) : 855 - 864
  • [29] Multi-Scale Applicability Analysis of Three Ecological Network Construction Methods in Resilience Assessment
    Huang, Xinyuan
    Wang, Xiyu
    Li, Jiaxin
    Zhang, Mengxian
    Chen, Shensheng
    Xu, Bin
    Nie, Wenbin
    LAND DEGRADATION & DEVELOPMENT, 2025, 36 (05) : 1594 - 1613
  • [30] On knowledge acquisition in multi-scale decision systems
    Shen-Ming Gu
    Wei-Zhi Wu
    International Journal of Machine Learning and Cybernetics, 2013, 4 : 477 - 486