Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Geometry and invariance in kernel based methods
Advances in kernel methods
Optimizing classifiers for imbalanced training sets
Proceedings of the 1998 conference on Advances in neural information processing systems II
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Data Mining and Knowledge Discovery
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Quantifying trends accurately despite classifier error and class imbalance
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning concepts from large scale imbalanced data sets using support cluster machines
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
Floatcascade learning for fast imbalanced web mining
Proceedings of the 17th international conference on World Wide Web
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Quantifying counts and costs via classification
Data Mining and Knowledge Discovery
Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments
International Journal of Computer Integrated Manufacturing
A Method to Classify Data by Fuzzy Rule Extraction from Imbalanced Datasets
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Application of artificial intelligence to operational real-time clear-air turbulence prediction
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Margin calibration in SVM class-imbalanced learning
Neurocomputing
On strategies for imbalanced text classification using SVM: A comparative study
Decision Support Systems
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Information Sciences: an International Journal
Proceedings of the international conference on Multimedia information retrieval
Expert Systems with Applications: An International Journal
Rectangular basis functions applied to imbalanced datasets
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An asymmetric classifier based on partial least squares
Pattern Recognition
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
CODE: a data complexity framework for imbalanced datasets
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization
Expert Systems with Applications: An International Journal
Class information adapted kernel for support vector machine
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Borderline over-sampling for imbalanced data classification
International Journal of Knowledge Engineering and Soft Data Paradigms
Support vector machines using Bayesian-based approach in the issue of unbalanced classifications
Expert Systems with Applications: An International Journal
Asymmetric Kernel scaling for imbalanced data classification
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
A learning strategy for highly imbalanced classification
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Clustering based bagging algorithm on imbalanced data sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
FISA: feature-based instance selection for imbalanced text classification
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Expert Systems with Applications: An International Journal
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
A normal distribution-based over-sampling approach to imbalanced data classification
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
A conversation with Dr. Edward Y. Chang
ACM SIGKDD Explorations Newsletter
Improving ANNs performance on unbalanced data with an AUC-Based learning algorithm
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
Adjusted F-measure and kernel scaling for imbalanced data learning
Information Sciences: an International Journal
A Fast Multiclass Classification Algorithm Based on Cooperative Clustering
Neural Processing Letters
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
Imbalanced evolving self-organizing learning
Neurocomputing
Hi-index | 0.01 |
An imbalanced training data set can pose serious problems for many real-world data mining tasks that employ SVMs to conduct supervised learning. In this paper, we propose a kernel-boundary-alignment algorithm, which considers THE training data imbalance as prior information to augment SVMs to improve class-prediction accuracy. Using a simple example, we first show that SVMs can suffer from high incidences of false negatives when the training instances of the target class are heavily outnumbered by the training instances of a nontarget class. The remedy we propose is to adjust the class boundary by modifying the kernel matrix, according to the imbalanced data distribution. Through theoretical analysis backed by empirical study, we show that our kernel-boundary-alignment algorithm works effectively on several data sets.