Exploratory undersampling for class-imbalance learning

Authors:
Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou
Affiliations:
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2009

Citing 34
Cited 43

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Original Contribution: Stacked generalization

Neural Networks
Bagging predictors

Machine Learning
Boosting and Rocchio applied to text filtering

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Optimizing classifiers for imbalanced training sets

Proceedings of the 1998 conference on Advances in neural information processing systems II
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
Random Forests

Machine Learning
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Stochastic gradient boosting

Computational Statistics & Data Analysis - Nonlinear methods and data mining
AdaCost: Misclassification Cost-Sensitive Boosting

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Brief Introduction to Boosting

IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Extracting symbolic rules from trained neural network ensembles

AI Communications - Special issue on Artificial intelligence advances in China
Cost-Sensitive Learning by Cost-Proportionate Example Weighting

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Robust Real-Time Face Detection

International Journal of Computer Vision
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
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques

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
Exploratory Under-Sampling for Class-Imbalance Learning

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Asymmetric boosting

Proceedings of the 24th international conference on Machine learning
Fast Asymmetric Learning for Cascade Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
NeC4.5: Neural Ensemble Based C4.5

IEEE Transactions on Knowledge and Data Engineering
Cocktail Ensemble for Regression

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on 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
Issues in stacked generalization

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
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Learning classifiers from imbalanced data based on biased minimax probability machine

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble

IEEE Transactions on Information Technology in Biomedicine

Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing Image

MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Combination Classification Algorithm Based on Outlier Detection and C4.5

ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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
Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluations of multi-learner approaches for concept indexing in video documents

RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
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
A multi-objective optimisation approach for class imbalance learning

Pattern Recognition
Software defect detection with rocus

Journal of Computer Science and Technology
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
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

Knowledge-Based Systems
Class imbalance methods for translation initiation site recognition in DNA sequences

Knowledge-Based Systems
Iranian cancer patient detection using a new method for learning at imbalanced datasets

IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Exploiting online music tags for music emotion classification

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Ensembles of decision trees for imbalanced data

MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Mitotic HEp-2 cells recognition under class skew

ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Detection of cancer patients using an innovative method for learning at imbalanced datasets

RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A task level metric for measuring web search satisfaction and its application on improving relevance estimation

Proceedings of the 20th ACM international conference on Information and knowledge management
Ensemble multi-instance multi-label learning approach for video annotation task

MM '11 Proceedings of the 19th ACM international conference on Multimedia
Using model trees and their ensembles for imbalanced data

CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Predicting the need for CT imaging in children with minor head injury using an ensemble of Naive Bayes classifiers

Artificial Intelligence in Medicine
Mixed-sampling approach to unbalanced data distributions: a case study involving Leukemia's document profiling

WSEAS Transactions on Information Science and Applications
Inverse random under sampling for class imbalance problem and its application to multi-label classification

Pattern Recognition
Semi-supervised learning for imbalanced sentiment classification

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Active learning with multiple classifiers for multimedia indexing

Multimedia Tools and Applications
Generating balanced classifier-independent training samples from unlabeled data

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
Extensions of ant-miner algorithm to deal with class imbalance problem

IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Nonlinear transformation of term frequencies for term weighting in text categorization

Engineering Applications of Artificial Intelligence
ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data

Neurocomputing
An efficient and simple under-sampling technique for imbalanced time series classification

Proceedings of the 21st ACM international conference on Information and knowledge management
Sample cutting method for imbalanced text sentiment classification based on BRC

Knowledge-Based Systems
A comparative study of sampling methods and algorithms for imbalanced time series classification

AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling

Neurocomputing
Feature selection for high-dimensional imbalanced data

Neurocomputing
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

Pattern Recognition
Large scale visual classification with many classes

MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Evaluation of sampling methods for learning from imbalanced data

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Classifying microblogs for disasters

Proceedings of the 18th Australasian Document Computing Symposium
Graph classification with imbalanced class distributions and noise

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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
Boosting weighted ELM for imbalanced learning

Neurocomputing
Learning a taxonomy of predefined and discovered activity patterns

Journal of Ambient Intelligence and Smart Environments

Quantified Score

Hi-index	0.00

Visualization

Abstract

Undersampling is a popular method in dealing with class-imbalance problems, which uses only a subset of the majority class and thus is very efficient. The main deficiency is that many majority class examples are ignored. We propose two algorithms to overcome this deficiency. EasyEnsemble samples several subsets from the majority class, trains a learner using each of them, and combines the outputs of those learners. BalanceCascade trains the learners sequentially, where in each step, the majority class examples that are correctly classified by the current trained learners are removed from further consideration. Experimental results show that both methods have higher Area Under the ROC Curve, F-measure, and G-mean values than many existing class-imbalance learning methods. Moreover, they have approximately the same training time as that of undersampling when the same number of weak classifiers is used, which is significantly faster than other methods.