Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
The class imbalance problem: A systematic study
Intelligent Data Analysis
On the k-NN performance in a challenging scenario of imbalance and overlapping
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
International Journal of Approximate Reasoning
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Information Sciences: an International Journal
Learning from imbalanced data in presence of noisy and borderline examples
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Automatic recognition of complete palynomorphs in digital images
Machine Vision and Applications
Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Class imbalance methods for translation initiation site recognition in DNA sequences
Knowledge-Based Systems
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A proposal for improving the accuracy of linguistic modeling
IEEE Transactions on Fuzzy Systems
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Linguistic modeling by hierarchical systems of linguistic rules
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out. Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behavior in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model. An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.