Communications of the ACM
A general lower bound on the number of examples needed for learning
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
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
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Making large-scale support vector machine learning practical
Advances in kernel methods
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
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
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth 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
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Editorial: special issue on learning from imbalanced data sets
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
A Bayesian network framework for reject inference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Sampling-based sequential subgroup mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
One-Benefit learning: cost-sensitive learning with restricted cost information
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Error limiting reductions between classification tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Relating reinforcement learning performance to classification performance
ICML '05 Proceedings of the 22nd international conference on Machine learning
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Predicting Software Escalations with Maximum ROI
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Using secure coprocessors for privacy preserving collaborative data mining and analysis
DaMoN '06 Proceedings of the 2nd international workshop on Data management on new hardware
Pareto optimal linear classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Experience-efficient learning in associative bandit problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Maximum profit mining and its application in software development
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Local decomposition for rare class analysis
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Perceptron and SVM learning with generalized cost models
Intelligent Data Analysis
Extending boosting for large scale spoken language understanding
Machine Learning
Extending boosting for large scale spoken language understanding
Machine Learning
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Multi-class cost-sensitive boosting with p-norm loss functions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatically countering imbalance and its empirical relationship to cost
Data Mining and Knowledge Discovery
Cost-sensitive learning with conditional Markov networks
Data Mining and Knowledge Discovery
The method for solving two types of errors in customer segmentation on unbalanced data
Proceedings of the 10th international conference on Electronic commerce
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Roulette Sampling for Cost-Sensitive Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Sample Selection Bias Correction Theory
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Risk-Sensitive Learning via Minimization of Empirical Conditional Value-at-Risk
IEICE - Transactions on Information and Systems
Search-based structured prediction
Machine Learning
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The offset tree for learning with partial labels
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Based Sampling of Individual Instances
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Thresholding for making classifiers cost-sensitive
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Exploiting contexts to deal with uncertainty in classification
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Cost-sensitive learning based on Bregman divergences
Machine Learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
An empirical study of the noise impact on cost-sensitive learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An integrated framework for de-identifying unstructured medical data
Data & Knowledge Engineering
Automatic link detection: a sequence labeling approach
Proceedings of the 18th ACM conference on Information and knowledge management
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
COG: local decomposition for rare class analysis
Data Mining and Knowledge Discovery
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning to evaluate the visual quality of web pages
Proceedings of the 19th international conference on World wide web
On selection and combination of weak learners in AdaBoost
Pattern Recognition Letters
Making class bias useful: a strategy of learning from imbalanced data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Large margin cost-sensitive learning of conditional random fields
Pattern Recognition
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting cost-sensitive learning for reject option
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An evaluation of feature sets and sampling techniques for de-identification of medical records
Proceedings of the 1st ACM International Health Informatics Symposium
Customer Validation of Commercial Predictive Models
Proceedings of the 2010 conference on Data Mining for Business Applications
Evaluating the visual quality of web pages using a computational aesthetic approach
Proceedings of the fourth ACM international conference on Web search and data mining
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
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
The Journal of Machine Learning Research
Learning to detect malicious URLs
ACM Transactions on Intelligent Systems and Technology (TIST)
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Joint training of dependency parsing filters through latent support vector machines
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Classifying severely imbalanced data
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites
ACM Transactions on Information and System Security (TISSEC)
Information, Divergence and Risk for Binary Experiments
The Journal of Machine Learning Research
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Fast support vector machines for structural Kernels
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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
Expert Systems with Applications: An International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary induction of cost-sensitive decision trees
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Poster: online spam filtering in social networks
Proceedings of the 18th ACM conference on Computer and communications security
Shedding light on the asymmetric learning capability of AdaBoost
Pattern Recognition Letters
Knowledge-Based sampling for subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Sensitive error correcting output codes
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Expert Systems with Applications: An International Journal
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
Artificial Intelligence in Medicine
Towards cost-sensitive learning for real-world applications
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Journal of Computer and System Sciences
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Per-patch descriptor selection using surface and scene properties
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment
Journal of Management Information Systems
Active Sampling for Entity Matching with Guarantees
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Cost-sensitive learning for large-scale hierarchical classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Algorithm portfolios based on cost-sensitive hierarchical clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Information Sciences: an International Journal
A plug-in approach to neyman-pearson classification
The Journal of Machine Learning Research
Improving ranking performance with cost-sensitive ordinal classification via regression
Information Retrieval
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
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We propose and evaluate a family of methods for convertingclassifier learning algorithms and classification theoryinto cost-sensitive algorithms and theory. The proposedconversion is based on cost-proportionate weighting of thetraining examples, which can be realized either by feedingthe weights to the classification algorithm (as often done inboosting), or by careful subsampling. We give some theoreticalperformance guarantees on the proposed methods,as well as empirical evidence that they are practical alternativesto existing approaches. In particular, we proposecosting, a method based on cost-proportionate rejectionsampling and ensemble aggregation, which achievesexcellent predictive performance on two publicly availabledatasets, while drastically reducing the computation requiredby other methods.