A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fuzzy Sets and Systems - Special issue on fuzzy neural control
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
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
Extreme re-balancing for SVMs: a case study
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
Fuzzy Rule Extraction from Support Vector Machines
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Feature Selection using Fuzzy Support Vector Machines
Fuzzy Optimization and Decision Making
Exploratory Under-Sampling for Class-Imbalance Learning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Classification of fuzzy measures
Fuzzy Sets and Systems
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Fuzzy Support Vector Machine Based on Vague Sets for Credit Assessment
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
SVM Learning from Imbalanced Data by GA Sampling for Protein Domain Prediction
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
International Journal of Approximate Reasoning
Bioinformatics
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
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
Fuzzy support vector machines based on spherical regions
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Fuzzy output support vector machines for classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Fuzzy support vector machines based on λ-cut
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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
z-SVM: an SVM for improved classification of imbalanced data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Prediction of liquefaction potential based on CPT up-sampling
Computers & Geosciences
Information Sciences: an International Journal
A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Neurocomputing
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
Evaluation of a new hybrid algorithm for highly imbalanced classification problems
International Journal of Hybrid Intelligent Systems
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
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Support vector machines (SVMs) is a popular machine learning technique, which works effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs produce suboptimal classification models. On the other hand, the SVM algorithm is sensitive to outliers and noise present in the datasets. Therefore, although the existing class imbalance learning (CIL) methods can make SVMs less sensitive to class imbalance, they can still suffer from the problem of outliers and noise. Fuzzy SVMs (FSVMs) is a variant of the SVM algorithm, which has been proposed to handle the problem of outliers and noise. In FSVMs, training examples are assigned different fuzzy-membership values based on their importance, and these membership values are incorporated into the SVM learning algorithm to make it less sensitive to outliers and noise. However, like the normal SVM algorithm, FSVMs can also suffer from the problem of class imbalance. In this paper, we present a method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise. We thoroughly evaluated the proposed FSVM-CIL method on ten real-world imbalanced datasets and compared its performance with five existing CIL methods, which are available for normal SVM training. Based on the overall results, we can conclude that the proposed FSVM-CIL method is a very effective method for CIL, especially in the presence of outliers and noise in datasets.