On the Jaccard Index with Degree of Optimism in Ranking Fuzzy Numbers

被引:0
|
作者
Ramli, Nazirah [1 ]
Mohamad, Daud [2 ]
机构
[1] Univ Teknol MARA, Fac Comp & Math Sci, Dept Math & Stat, Bandar Jengka 26400, Pahang, Malaysia
[2] Univ Teknol MARA, Fac Math & Comp Sci, Dept Math, Shah Alam 40450, Malaysia
来源
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, PT II | 2010年 / 81卷
关键词
degree of optimism; fuzzy total evidence; Jaccard index; ranking fuzzy numbers; DISTANCE; AREA;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ranking of fuzzy numbers plays an important role in practical use and has become a prerequisite procedure for decision-making problems in fuzzy environment. Jaccard index similarity measure has been introduced in ranking the fuzzy numbers where fuzzy maximum, fuzzy minimum, fuzzy evidence and fuzzy total evidence are used in determining the ranking. However, the fuzzy total evidence is obtained by using the mean aggregation which can only represent the neutral decision maker's perspective. In this paper, the degree of optimism concept winch represents all types of decision maker's perspectives is applied in calculating the fuzzy total evidence. Thus, the proposed method is capable to rank fuzzy numbers based on optimistic, pessimistic and neutral decision maker's perspective. Some properties which can simplify the ranking procedure are also presented.
引用
收藏
页码:383 / +
页数:2
相关论文
共 50 条
  • [1] RANKING FUZZY NUMBERS WITH INDEX OF OPTIMISM
    KIM, K
    PARK, KS
    FUZZY SETS AND SYSTEMS, 1990, 35 (02) : 143 - 150
  • [2] On the Jaccard Index Similarity Measure in Ranking Fuzzy Numbers
    Ramli, Nazirah
    Mohamad, Daud
    MATEMATIKA, 2009, 25 (02) : 157 - 165
  • [3] A Function Principle Approach to Jaccard Ranking Fuzzy Numbers
    Ramli, Nazirah
    Mohamad, Daud
    2009 INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION, 2009, : 324 - +
  • [4] The Method for Ranking Fuzzy Numbers Based on the Centroid Index and the Fuzziness Degree
    Wang, Zhong-xing
    Li, Jian
    Gao, Shan-lin
    FUZZY INFORMATION AND ENGINEERING, VOLUME 2, 2009, 62 : 1335 - 1342
  • [5] Ranking fuzzy numbers based on the mean and fuzzy degree of fuzzy number
    Wang, Guixiang
    Zhou, Guangtao
    ADVANCES IN MANUFACTURING TECHNOLOGY, PTS 1-4, 2012, 220-223 : 2102 - 2108
  • [6] A revision on area ranking and deviation degree methods of ranking fuzzy numbers
    Ghasemi, R.
    Nikfar, M.
    Roghanian, E.
    SCIENTIA IRANICA, 2015, 22 (03) : 1142 - 1154
  • [7] Ranking Fuzzy Numbers by Similarity Measure Index
    Hajjari, Tayebeh
    4TH INTERNATIONAL CONFERENCE ON ALGORITHMS, COMPUTING AND SYSTEMS, ICACS 2020, 2020, : 12 - 16
  • [8] A Preference Index for Ranking Closed Intervals and Fuzzy Numbers
    Hesamian, Gholamreza
    Akbari, Mohamad Ghasem
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2017, 25 (05) : 741 - 757
  • [9] Ranking fuzzy numbers using reference sets and degree of dominance
    Yeh, CH
    Deng, H
    10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE, 2001, : 1167 - 1170
  • [10] A NEW TECHNIQUE FOR COMPAIRING FUZZY NUMBERS USING AN INDEX OF OPTIMISM
    阿谢德
    史习智
    徐济鋆
    JournalofShanghaiJiaotongUniversity, 2002, (02) : 147 - 151