Improved Estimates for the Accuracy of Small Disjuncts
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
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
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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
Machine Learning
Data Mining and Knowledge Discovery
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
High-Order Pattern Discovery from Discrete-Valued Data
IEEE Transactions on Knowledge and Data Engineering
Pruning Decision Trees with Misclassification Costs
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
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
From Association to Classification: Inference Using Weight of Evidence
IEEE Transactions on Knowledge and Data Engineering
Learning classifier models for predicting rare phenomena
Learning classifier models for predicting rare phenomena
One-class svms for document classification
The Journal of Machine Learning Research
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
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
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
The class imbalance problem: A systematic study
Intelligent Data Analysis
Cost-sensitive boosting for classification of imbalanced data
Cost-sensitive boosting for classification of imbalanced data
SMOTE: synthetic minority over-sampling technique
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
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Using Cost-Sensitive Learning to Determine Gene Conversions
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Knowledge discovery from imbalanced and noisy data
Data & Knowledge Engineering
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Support vector self-organizing learning for imbalanced medical data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Artificial Intelligence Review
Information Sciences: an International Journal
Improving software-quality predictions with data sampling and boosting
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Boosting with pairwise constraints
Neurocomputing
Cost-sensitive boosting neural networks for software defect prediction
Expert Systems with Applications: An International Journal
Study on customer churn prediction methods based on multiple classifiers combination
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An asymmetric classifier based on partial least squares
Pattern Recognition
Large margin cost-sensitive learning of conditional random fields
Pattern Recognition
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
Expert Systems with Applications: An International Journal
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
Induction and pruning of classification rules for prediction of microseismic hazards in coal mines
Expert Systems with Applications: An International Journal
Class imbalance methods for translation initiation site recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Audio tag annotation and retrieval using tag count information
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Class imbalance methods for translation initiation site recognition in DNA sequences
Knowledge-Based Systems
Feature selection for translation initiation site recognition
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Translation initiation site recognition by means of evolutionary response surfaces
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
An evolutionary algorithm for gene structure prediction
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
A unifying view on dataset shift in classification
Pattern Recognition
Adaptive boosting for transfer learning using dynamic updates
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Metric anomaly detection via asymmetric risk minimization
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
QBoost: Predicting quantiles with boosting for regression and binary classification
Expert Systems with Applications: An International Journal
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
Gradient boosting trees for auto insurance loss cost modeling and prediction
Expert Systems with Applications: An International Journal
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Instance selection for class imbalanced problems by means of selecting instances more than once
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
On Equivalence Relationships Between Classification and Ranking Algorithms
The Journal of Machine Learning Research
Shedding light on the asymmetric learning capability of AdaBoost
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Asymmetric constraint optimization based adaptive boosting for cascade face detector
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Editorial: Large scale instance selection by means of federal instance selection
Data & Knowledge Engineering
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
Mathematical and Computer Modelling: An International Journal
Computer Methods and Programs in Biomedicine
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting seminal quality with artificial intelligence methods
Expert Systems with Applications: An International Journal
A noise-detection based AdaBoost algorithm for mislabeled data
Pattern Recognition
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
Over-Sampling from an auxiliary domain
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Oversampling methods for classification of imbalanced breast cancer malignancy data
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment
Journal of Management Information Systems
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
Double-base asymmetric AdaBoost
Neurocomputing
Early prediction on imbalanced multivariate time series
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Risk prediction of femoral neck osteoporosis using machine learning and conventional methods
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
Cost-sensitive decision tree ensembles for effective imbalanced classification
Applied Soft Computing
Applying Ant Colony Optimization to configuring stacking ensembles for data mining
Expert Systems with Applications: An International Journal
Multi-class boosting with asymmetric binary weak-learners
Pattern Recognition
Boosting weighted ELM for imbalanced learning
Neurocomputing
Imbalanced evolving self-organizing learning
Neurocomputing
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
Robust classification of imbalanced data using one-class and two-class SVM-based multiclassifiers
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a successful meta-technique for improving classification accuracy. The insight gained from a comprehensive analysis of the AdaBoost algorithm in terms of its advantages and shortcomings in tacking the class imbalance problem leads to the exploration of three cost-sensitive boosting algorithms, which are developed by introducing cost items into the learning framework of AdaBoost. Further analysis shows that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss. These boosting algorithms are also studied with respect to their weighting strategies towards different types of samples, and their effectiveness in identifying rare cases through experiments on several real world medical data sets, where the class imbalance problem prevails.