Knowledge base to fuzzy information granule: A review from the interpretability-accuracy perspective

被引:29
作者
Ahmed, Md. Manjur [1 ,2 ]
Isa, Nor Ashidi Mat [2 ]
机构
[1] Univ Malaysia Pahang, Fac Comp Syst & Software Engn, Kuantan 26300, Pahang, Malaysia
[2] Univ Sains Malaysia, Sch Elect & Elect Engn, Imaging & Intelligent Syst Res Team ISRT, Engn Campus, Nibong Tebal 14300, Penang, Malaysia
关键词
Fuzzy information granule; Interpretability-accuracy tradeoff; Granularity-accuracy dilemma; Overfitting/underfitting situation; Conflict situation; GROUP DECISION-MAKING; NEURAL-NETWORKS; RULE WEIGHTS; SYSTEMS; DESIGN; MODEL; METHODOLOGY; IDENTIFICATION; REDUCTION; PRINCIPLE;
D O I
10.1016/j.asoc.2016.12.055
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fuzzy information granules indicate sufficiently interpretable fuzzy sets for achieving a high level of human cognitive abstraction. Furthermore, granularity, complexity, and accuracy are associated with fuzzy information granules. Measuring granularity is a promising means of verifying the effectiveness of the fuzzy granular model. Higher granularity indicates fine partitions, whereas coarser partitions suggest lower granularity. Therefore, accuracy is directly proportional to the granularity, such that, the higher the granularity, the more accurate and more complex the model is. Consequently, the granularity-simplicity tradeoff is also a significant criterion in considering the interpretability-accuracy tradeoff. This paper thoroughly reviews diverse ideas to understand the fuzzy information granule and addresses a sensible compromise between interpretability-accuracy and granularity-simplicity. Those requirements contradict each other, thus certain conceptual and mathematical considerations are necessary in designing a granular framework. Moreover, a double axis taxonomy is introduced in this paper: "complexity-based granularity versus semantic-based granularity" (which considers granularity measures) and "granular partition level versus granular rule base level" (regarding knowledge base stages). However, several constraints should be considered in designing a granular framework such as the granularity-accuracy dilemma, the overfitting/underfitting situation, the granular rule base level conflict, the interpretability constraint threshold, the stability-plasticity dilemma, and the parameter optimization. This paper primarily aims to present a conceptual framework to better understand existing methods, as well as how these methods can inspire future research. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:121 / 140
页数:20
相关论文
共 92 条
  • [1] Information granularity model for evolving context-based fuzzy system
    Ahmed, Md. Manjur
    Isa, Nor Ashidi Mat
    [J]. APPLIED SOFT COMPUTING, 2015, 33 : 183 - 196
  • [2] Recursive construction of output-context fuzzy systems for the condition monitoring of electrical hotspots based on infrared thermography
    Ahmed, Md. Manjur
    Huda, A. S. N.
    Isa, Nor Ashidi Mat
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 39 : 120 - 131
  • [3] HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism
    Alonso, Jose M.
    Magdalena, Luis
    Guillaume, Serge
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2008, 23 (07) : 761 - 794
  • [4] Group Decision Making with Incomplete Fuzzy Linguistic Preference Relations
    Alonso, S.
    Cabrerizo, F. J.
    Chiclana, F.
    Herrera, F.
    Herrera-Viedma, E.
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2009, 24 (02) : 201 - 222
  • [5] RSPOP: Rough set-based pseudo outer-product fuzzy rule identification algorithm
    Ang, KK
    Quek, C
    [J]. NEURAL COMPUTATION, 2005, 17 (01) : 205 - 243
  • [6] [Anonymous], GRANULAR COMPUTING
  • [7] IVTURS: A Linguistic Fuzzy Rule-Based Classification System Based On a New Interval-Valued Fuzzy Reasoning Method With Tuning and Rule Selection
    Antonio Sanz, Jose
    Fernandez, Alberto
    Bustince, Humberto
    Herrera, Francisco
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2013, 21 (03) : 399 - 411
  • [8] Bezdek J. C., 1992, International Journal of Approximate Reasoning, V6, P85, DOI 10.1016/0888-613X(92)90013-P
  • [9] Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades
    Bosque, G.
    del Campo, I.
    Echanobe, J.
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2014, 32 : 283 - 331
  • [10] A new model for linguistic summarization of heterogeneous data: an application to tourism web data sources
    Carrasco, Ramon A.
    Villar, Pedro
    [J]. SOFT COMPUTING, 2012, 16 (01) : 135 - 151