Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Data Mining and Knowledge Discovery
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A weighted fuzzy classifier and its application to image processing tasks
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
International Journal of Intelligent Systems
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
The class imbalance problem: A systematic study
Intelligent Data Analysis
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
Classification of weld flaws with imbalanced class data
Expert Systems with Applications: An International Journal
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
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Neighbor-weighted K-nearest neighbor for unbalanced text corpus
Expert Systems with Applications: An International Journal
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining
Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On choosing models for linguistic connector words for Mamdani fuzzy logic systems
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Kernel-Based Two-Class Classifier for Imbalanced Data Sets
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
An application of fuzzy information granulation in the emerging area of online sports
Expert Systems with Applications: An International Journal
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Design of real-time fuzzy bus holding system for the mass rapid transit transfer system
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Classification with imbalanced data-sets supposes a new challenge for researches in the framework of data mining. This problem appears when the number of examples that represents one of the classes of the data-set (usually the concept of interest) is much lower than that of the other classes. In this manner, the learning model must be adapted to this situation, which is very common in real applications. In this paper, we will work with fuzzy rule based classification systems using a preprocessing step in order to deal with the class imbalance. Our aim is to analyze the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets by means of the application of an adaptive inference system with parametric conjunction operators. Our results shows empirically that the use of the this parametric conjunction operators implies a higher performance for all data-sets with different imbalanced ratios.