The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Data mining for improved cardiac care
ACM SIGKDD Explorations Newsletter
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Fuzzy relevance vector machine for learning from unbalanced data and noise
Pattern Recognition Letters
Generating fuzzy rules from training instances for fuzzy classification systems
Expert Systems with Applications: An International Journal
Bayes Vector Quantizer for Class-Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Bioinformatics
IEEE Transactions on Knowledge and Data Engineering
Several SVM Ensemble Methods Integrated with Under-Sampling for Imbalanced Data Learning
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
IEEE Transactions on Neural Networks
Robust kernel discriminant analysis using fuzzy memberships
Pattern Recognition
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting prediction accuracy on imbalanced datasets with SVM ensembles
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
IEEE Transactions on Signal Processing - Part I
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Discriminative Feature Selection by Nonparametric Bayes Error Minimization
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Neural Networks
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We develop a novel classifier in a kernel feature space, which can be used to handle the class imbalanced problem in the presence of noise and outliers. In many applications, each input point may not be fully assigned to one of two classes or multiclasses. Based on the Laplacian classifier (LC), we applied a fuzzy membership to each input point and reformulate LC so that different input points can make different contributions to the learning process. We called the proposed method the fuzzy Laplacian classifier (FLC). We thoroughly evaluated the proposed FLC method on two simulation data examples and ten real-world data examples and compare its performance with support vector machine (SVM), fuzzy support vector machine (FSVM), fuzzy support vector machines for class imbalance learning (FSVM-CIL) and LC. Based on the overall results obtained in the experiments, we can conclude that the proposed FLC method can not only result in better classification results than SVM, FSVM, FSVM-CIL and LC for the imbalanced data sets in the presence of noise or outliers, but also emphasize more to classify the least probableclass.