The optimization of objective weighting method based on relative importance

被引:4
|
作者
Jin Xing [1 ]
Zhang Wenshuo [1 ]
机构
[1] Changchun Univ Technol, Coll Elect & Elect Engn, Changchun, Peoples R China
关键词
objective weighting method optimization; index weight; the optimized grey relational analysis method; optimized mean error method; relative importance;
D O I
10.1109/ICMCCE51767.2020.00271
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The concept of "relative importance" is introduced to solve the problem of low weight differentiation obtained by objective weighting method. The experiment optimizes five objective weighting methods: grey relative analysis, mean square error method, coefficient of variance method, the entropy weight method and Gini coefficient method. The experiment resolves the index weight which is based on the relative importance between features. The experiment compared the difference of index weight between the original objective weighting method and the optimized objective weighting method. The conclusion is that the optimized grey relative analysis method and the optimized mean error method not only accord with the objective fact, but also improve the distinction between the weights of the indexes under the condition that the order of relative importance among the indexes remains the same.
引用
收藏
页码:1230 / 1233
页数:4
相关论文
共 50 条
  • [41] THE RELATIVE IMPORTANCE OF TREATMENT OUTCOMES - A DELPHI GROUP WEIGHTING IN MENTAL-HEALTH
    CLARK, A
    FRIEDMAN, MJ
    EVALUATION REVIEW, 1982, 6 (01) : 79 - 93
  • [42] On the choice of parameters for the weighting method in vector optimization
    L. M. Graña Drummond
    N. Maculan
    B. F. Svaiter
    Mathematical Programming, 2008, 112 : 273 - 273
  • [43] A Weighting Threshold Optimization Method in Speech Recognition
    Liu, Xuefei
    Lu, Linlin
    2009 ASIA-PACIFIC CONFERENCE ON INFORMATION PROCESSING (APCIP 2009), VOL 2, PROCEEDINGS, 2009, : 314 - 317
  • [44] Intelligent prediction and recommendation optimization method based on fuzzy clustering and time weighting
    Jiang Wei-Jin
    Chen Jia-Hui
    Xu Yu-Hui
    IIWAS2018: THE 20TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES, 2014, : 79 - 84
  • [45] On the choice of parameters for the weighting method in vector optimization
    Drummond, L. M. Grana
    Maculan, N.
    Svaiter, B. F.
    MATHEMATICAL PROGRAMMING, 2008, 112 (01) : 273 - 273
  • [46] A genetic method for designing TSK models based on objective weighting: application to classification problems.
    S. E. Papadakis
    J. B. Theocharis
    Soft Computing, 2006, 10 : 805 - 824
  • [47] Constrained multi-objective optimization based on particle swarm optimization method
    Zhang, MH
    Ma, LH
    ICCC2004: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION VOL 1AND 2, 2004, : 1765 - 1771
  • [48] Hybrid feature selection and weighting method based on binary particle swarm optimization
    Severo, Diogo S.
    Verissimo, Everson
    Cavalcanti, George D. C.
    Ren, Tsang Ing
    2013 IEEE 25TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI), 2013, : 433 - 438
  • [49] Measuring Road Transport Sustainability Using MCDM-Based Entropy Objective Weighting Method
    Wang, Chia-Nan
    Le, Tran Quynh
    Chang, Kuei-Hu
    Dang, Thanh-Tuan
    SYMMETRY-BASEL, 2022, 14 (05):
  • [50] Weighting Matrix Selection Method for LQR Design Based on a Multi-objective Evolutionary Algorithm
    Nekoui, Mohammad Ali
    Bozorgi, Hassan Heidari Jame
    MANUFACTURING SCIENCE AND TECHNOLOGY, PTS 1-8, 2012, 383-390 : 1047 - 1054