Communications of the ACM - Special issue on parallelism
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural Network Classification and Prior Class Probabilities
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
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
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Automatically countering imbalance and its empirical relationship to cost
Data Mining and Knowledge Discovery
Cost-Sensitive Decision Trees with Pre-pruning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
An Empirical Study for the Multi-class Imbalance Problem with Neural Networks
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Error analysis in artificial neural networks: the imbalanced distribution case
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
A Weighted Rough Set Approach for Cost-Sensitive Learning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Cost-sensitive learning based on Bregman divergences
Machine Learning
Asymmetric Feature Selection for BGP Abnormal Events Detection
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Diversity exploration and negative correlation learning on imbalanced data sets
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Information Sciences: an International Journal
An investigation of neural network classifiers with unequal misclassification costs and group sizes
Decision Support Systems
Cost-sensitive boosting neural networks for software defect prediction
Expert Systems with Applications: An International Journal
Weighted rough set learning: towards a subjective approach
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Improving the performance of the RBF neural networks trained with imbalanced samples
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
SELDI-TOF-MS pattern analysis for cancer detection as a base for diagnostic software
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Probabilistic models for answer-ranking in multilingual question-answering
ACM Transactions on Information Systems (TOIS)
Spam filtering and email-mediated applications
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Cost-sensitive classifier evaluation using cost curves
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Exploring an improved decision tree based weights
ICNC'09 Proceedings of the 5th international conference on Natural computation
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
Multicategory nets of single-layer perceptrons: complexity and sample-size issues
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
An intraday market risk management approach based on textual analysis
Decision Support Systems
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
A hierarchical shrinking decision tree for imbalanced datasets
DNCOCO'06 Proceedings of the 5th WSEAS international conference on Data networks, communications and computers
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
Cost-sensitive neural networks and editing techniques for imbalance problems
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Expert Systems with Applications: An International Journal
Resampling methods versus cost functions for training an MLP in the class imbalance context
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Classification of high dimensional and imbalanced hyperspectral imagery data
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Margin-based over-sampling method for learning from imbalanced datasets
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
MultiCost: multi-stage cost-sensitive classification of Alzheimer's disease
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Imbalanced sentiment classification
Proceedings of the 20th ACM international conference on Information and knowledge management
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiple costs based decision making with back-propagation neural networks
Decision Support Systems
Expert Systems with Applications: An International Journal
Test cost constraint reduction with common cost
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Controlling multi-class error rates for MLP classifier by bias adjustment based on penalty matrix
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Towards cost-sensitive learning for real-world applications
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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
Semi-supervised learning for imbalanced sentiment classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
Attribute reduction of data with error ranges and test costs
Information Sciences: an International Journal
Building decision trees for the multi-class imbalance problem
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Exploratory class-imbalanced and non-identical data distribution in automatic keyphrase extraction
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
A competition strategy to cost-sensitive decision trees
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Computers in Biology and Medicine
Sample cutting method for imbalanced text sentiment classification based on BRC
Knowledge-Based Systems
RFM analysis for detecting future core technology
Proceedings of the 2012 ACM Research in Applied Computation Symposium
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Pattern Recognition Letters
A comparison study of cost-sensitive classifier evaluations
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Neurocomputing
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
A virtual mart for knowledge discovery in databases
Information Systems Frontiers
F-measure as the error function to train neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Expert Systems with Applications: An International Journal
A loan default discrimination model using cost-sensitive support vector machine improved by PSO
Information Technology and Management
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Boosting weighted ELM for imbalanced learning
Neurocomputing
Multimedia Tools and Applications
Cost-sensitive three-way email spam filtering
Journal of Intelligent Information Systems
Information Sciences: an International Journal
Imbalanced evolving self-organizing learning
Neurocomputing
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
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This paper studies empirically the effect of sampling and threshold-moving in training cost-sensitive neural networks. Both oversampling and undersampling are considered. These techniques modify the distribution of the training data such that the costs of the examples are conveyed explicitly by the appearances of the examples. Threshold-moving tries to move the output threshold toward inexpensive classes such that examples with higher costs become harder to be misclassified. Moreover, hard-ensemble and soft-ensemble, i.e., the combination of above techniques via hard or soft voting schemes, are also tested. Twenty-one UCI data sets with three types of cost matrices and a real-world cost-sensitive data set are used in the empirical study. The results suggest that cost-sensitive learning with multiclass tasks is more difficult than with two-class tasks, and a higher degree of class imbalance may increase the difficulty. It also reveals that almost all the techniques are effective on two-class tasks, while most are ineffective and even may cause negative effect on multiclass tasks. Overall, threshold-moving and soft-ensemble are relatively good choices in training cost-sensitive neural networks. The empirical study also suggests that some methods that have been believed to be effective in addressing the class imbalance problem may, in fact, only be effective on learning with imbalanced two-class data sets.