The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Robust Classification for Imprecise Environments
Machine Learning
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
One-class svms for document classification
The Journal of Machine Learning Research
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
Classification and knowledge discovery in protein databases
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Granular clustering: a granular signature of data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
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Scheduling environments are usually dynamic and vary with time. It is necessary that the scheduling method is flexible enough for modifications or changes during production, without interrupting actual operations. Recent researches indicate that applying inductive learning technologies is one of the useful ways to solve these kinds of problems. However, when learning from imbalanced data (almost all the examples are labelled as one class while far fewer objects are labelled as the other class), these methods have poor predictive ability to identify minority instances. This is because most inductive learning algorithms assume that maximizing accuracy on a full range of cases is the goal, and this results in very poor performance for cases associated with the low-frequency class. In this study, we introduce a novel knowledge acquisition algorithm called 'granular computing model' for imbalanced data and integrate this method into a scheduler within a simulated flexible manufacturing system (FMS) environment. Compared with costs adjusting, cluster-based sampling techniques and decision tree (C 4.5), the experimental results indicate that our approach can dramatically increase the predictive ability of minority examples while improving classification performances.