Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets

被引：0

作者：

Fernandez, Alberto ^{[1
]}

Berlanga, Francisco J. ^{[2
]}

del Jesus, Maria J. ^{[3
]}

Herrera, Francisco ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain

[2] Univ Zaragoza, Dept Comp Sci & Syst Engn, Zaragoza, Spain

[3] Univ Jaen, Dept Comp Sci, Jaen, Spain

来源：

PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | 2009年

关键词：

Fuzzy Rule-Based Classification Systems; Genetic Fuzzy Systems; Genetic Programming; Imbalanced Data-Sets; Interpretability; ACCURACY; SYSTEMS; PERFORMANCE; CLASSIFIERS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Classification in imbalanced domains is an important problem in Data Mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. The aim of this work is to study the behaviour of the GP-COACH algorithm in the scenario of data-sets with high imbalance, analysing both the performance and the interpretability of the obtained fuzzy models. To develop the experimental study we will compare this approach with a well-known fuzzy rule learning algorithm, the Chi et al.'s method, and an algorithm of reference in the field of imbalanced data-sets, the C4.5 decision tree.

引用

页码：42 / 47

页数：6

共 50 条

[41] One-sided fuzzy SVM based on sphere for imbalanced data sets learning
Han, Hui
Mao, Binghuan
Lv, Hairong
Zhuo, Qing
Wang, Wenyuan
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS, 2007, : 166 - +
[42] Multiobjective Genetic Generation of Fuzzy Classifiers using the Iterative Rule Learning
Cardenas, Edward Hinojosa
Camargo, Heloisa A.
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2012,
[43] Evolving fuzzy rule based controllers using genetic algorithms
Carse, B
Fogarty, TC
Munro, A
FUZZY SETS AND SYSTEMS, 1996, 80 (03) : 273 - 293
[44] On the Impact of the Objective Function on Imbalanced Data using Cartesian Genetic Programming Neuroevolutionary Approaches
Melo Neto, Johnathan M.
Bernardino, Heder S.
Barbosa, Helio J. C.
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, : 1860 - 1867
[45] Feature Selection for Multi-Class Imbalanced Data Sets Based on Genetic Algorithm
Du L.-M.
Xu Y.
Zhu H.
Ann. Data Sci., 3 (293-300): : 293 - 300
[46] A NEW HEURISTIC APPROACH FOR TRAINING DATA REDUCTION AND A GENETIC LEARNING METHOD FOR ACHEIVING COMPACT FUZZY RULE-BASED SYSTEMS
Tri Minh Huynh
PROCEEDINGS OF THE 2011 3RD INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGY AND ENGINEERING (ICSTE 2011), 2011, : 345 - 355
[47] Learning discriminant functions with fuzzy attributes for classification using genetic programming
Chien, BC
Lin, JY
Hong, TP
EXPERT SYSTEMS WITH APPLICATIONS, 2002, 23 (01) : 31 - 37
[48] An Embedded Unlabeled Data Partitioning PU-Learning Method Based on Genetic Programming
Zhou, Yu
Yang, Nanjian
Wang, Ran
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, : 1 - 11
[49] SIMULTANEOUS DESIGN OF MEMBERSHIP FUNCTIONS AND RULE SETS FOR FUZZY CONTROLLERS USING GENETIC ALGORITHMS
HOMAIFAR, A
MCCORMICK, E
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1995, 3 (02) : 129 - 139
[50] Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS
Araujo, Antonio E. de O.
Krohling, Renato A.
PROCEEDINGS OF THE 11TH CONFERENCE OF THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY (EUSFLAT 2019), 2019, 1 : 482 - 489

← 1 2 3 4 5 →