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] Ensemble Classifier Design by Parallel Distributed Implementation of Genetic Fuzzy Rule Selection for Large Data Sets [J].

Nojima, Yusuke ;

Mihara, Shingo ;

Ishibuchi, Hisao .

2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,

[42] A multiobjective genetic algorithm for feature selection and data base learning in fuzzy-rule based classification systems [J].

Cordón, O ;

Herrera, F ;

del Jesus, MJ ;

Magdalena, L ;

Sánchez, AM ;

Villar, P .

INTELLIGENT SYSTEMS FOR INFORMATION PROCESSING: FROM REPRESENTATION TO APPLICATIONS, 2003, :315-326

[43] GABoost: A Clustering Based Undersampling Algorithm for Highly Imbalanced Datasets Using Genetic Algorithm [J].

Ajilisa, O. A. ;

Jagathyraj, V. P. ;

Sabu, M. K. .

INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, 2019, 939 :235-246

[44] Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets [J].

Aljero, Mona Khalifa A. ;

Dimililer, Nazife .

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2023, 31 (01) :180-192

[45] Analyzing imbalanced online consumer review data in product design using geometric semantic genetic programming [J].

Chan, Kit Yan ;

Kwong, C. K. ;

Jiang, Huimin .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 105

[46] Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers [J].

Chan, K. Y. ;

Kwong, C. K. ;

Fogarty, T. C. .

INFORMATION SCIENCES, 2010, 180 (04) :506-518

[47] Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems [J].

Sathyan, Anoop ;

Ma, Jiaqi ;

Cohen, Kelly .

TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2021, 9 (01) :775-797

[48] Knowledge-based learning for modeling concrete compressive strength using genetic programming [J].

Tsai, Hsing-Chih ;

Liao, Min-Chih .

COMPUTERS AND CONCRETE, 2019, 23 (04) :255-265

[49] Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms [J].

Meng, Dan ;

Pei, Zheng .

NEUROCOMPUTING, 2012, 78 (01) :48-54

[50] Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data [J].

Marquez-Vera, Carlos ;

Cano, Alberto ;

Romero, Cristobal ;

Ventura, Sebastian .

APPLIED INTELLIGENCE, 2013, 38 (03) :315-330

← 1 2 3 4 5 →