Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Instance-Based Learning Algorithms
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
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
The class imbalance problem: A systematic study
Intelligent Data Analysis
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
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Expert Systems with Applications: An International Journal
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Most real-world data analyzed by classification techniques is imbalanced in terms of the proportion of examples available for each data class. This class imbalance problem would impede the performance of some standard classifiers since a modal-class pattern may cover many relatively weak interest patterns. This study presents a new learning algorithm based on conflict-sensitive contexture, which remedies the class imbalance problem by basing decisions on the inconsistency of the local entropy estimator. The study also adopts a new neuro-fuzzy network algorithm with multiple decision rules to a real-world banking case for mining very significant patterns. The proposed algorithm can attain accuracy for minority classes at classification from roughly 10% up to 71%. This work also elucidates these patterns of interests and suggests many business applications for them.