C4.5: programs for machine learning
C4.5: programs for machine learning
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The class imbalance problem: A systematic study
Intelligent Data Analysis
Fuzzy classifiers for imbalanced data sets
Fuzzy classifiers for imbalanced data sets
Data-driven fuzzy sets for classification
International Journal of Advanced Intelligence Paradigms
Fuzzy logic supported sketch based image information enhancement
International Journal of Advanced Intelligence Paradigms
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
A unified granular fuzzy-neuro min-max relational framework for medical diagnosis
International Journal of Advanced Intelligence Paradigms
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Classes of real world datasets have various properties (such as imbalance, size, complexity, and class distribution) that make the classification task more difficult. We investigate the robustness of six classification techniques over data having various combinations of the above mentioned properties. One artificial domain and six real world datasets are used in these experiments. Results of our analysis point to the frequency-based classifiers (such as the fuzzy and the Bayes classifiers) as being more robust over various imbalance, size, complexity, and training distribution.