An Algorithm To Building A Fuzzy Decision Tree For Data Classification Problem Based On  The Fuzziness Intervals Matching

Lan, Le Van Tuong; Hân, Nguyễn Mậu; Hao, Nguyen Cong

doi:10.15625/1813-9663/32/3/8801

Cited by 2 publications

(2 citation statements)

References 5 publications

(6 reference statements)

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“…Hedge algebras (HAs) [9,11,12,14,17,18] provide a mathematical formalism for designing the order based semantic structure of term domains of linguistic variables that can be applied to various application domains in the real life, such as fuzzy control [10,26,28,29], expert systems [12], data mining [5,13,15,16,25,40], fuzzy database [19,42], image processing [20], timetabling [31], etc. The crucial idea of the hedge algebra based approach is that it reflects the nature of fuzzy information by the fuzziness of information.…”

Section: Introductionmentioning

confidence: 99%

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

Phong

Dư

Thuy

et al. 2019

JCC

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During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.

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Section: Introductionmentioning

confidence: 99%

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

Phong

Dư

Thuy

et al. 2019

JCC

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“…Hedge algebras (HAs) [14][15][16][17][18] were introduced by Ho N. C. et al in the early 1990s and then HAs have been applied to many different fields such as data mining [19][20][21][22][23][24][25], fuzzy control [26][27][28], image processing [29], timetabling [30], etc. When applied to design the FRBCs, HAs take advantage of the algebraic approach which allows to design automatically the linguistic values integrated with their fuzzy sets from data [19,20] for the FRBCs.…”

Section: Introductionmentioning

confidence: 99%

A Hedge Algebras Based Reasoning Method for Fuzzy Rule Based Classifier

Phong

Dư

Thông

2019

JST

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The fuzzy rule based classifier (FRBC) design methods have intensively been being studied during last years. The ones designed by utilizing hedge algebras as a formalism to generate the optimal linguistic values along with their (triangular and trapezoidal) fuzzy sets based semantics for the FRBCs have been proposed. Those design methods generate the fuzzy sets based semantics because the classification reasoning method still bases on the fuzzy set theory. One question which has been arisen is whether there is a pure hedge algebras classification reasoning method so that the fuzzy sets based semantic of the linguistic values in the fuzzy rule bases can be replaced with the hedge algebras based semantic. This paper answers that question by presenting a fuzzy rule based classifier design method based on hedge algebras with a pure hedge algebras classification reasoning method. The experimental results over 17 real world datasets are compared to the existing methods based on hedge algebras and fuzzy sets theory showing that the proposed method is effective and produces good results.

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An Algorithm To Building A Fuzzy Decision Tree For Data Classification Problem Based On The Fuzziness Intervals Matching

Cited by 2 publications

References 5 publications

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

A Hedge Algebras Based Reasoning Method for Fuzzy Rule Based Classifier

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