Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
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
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
Classification Rule Learning with APRIORI-C
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Improving an Association Rule Based Classifier
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
World Wide Web
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Computing the minimum-support for mining frequent patterns
Knowledge and Information Systems
Automatically countering imbalance and its empirical relationship to cost
Data Mining and Knowledge Discovery
Efficient Pairwise Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
The Journal of Machine Learning Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Knowledge discovery from imbalanced and noisy data
Data & Knowledge Engineering
A comparison of different off-centered entropies to deal with class imbalance for decision trees
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Cost sensitive classification in data mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A learning strategy for highly imbalanced classification
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
On Equivalence Relationships Between Classification and Ranking Algorithms
The Journal of Machine Learning Research
Hellinger distance decision trees are robust and skew-insensitive
Data Mining and Knowledge Discovery
Contrast Data Mining: Concepts, Algorithms, and Applications
Contrast Data Mining: Concepts, Algorithms, and Applications
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Many applications deal with classification in multi-class imbalanced contexts. In such difficult situations, classical CBA-like approaches (Classification Based on Association rules) show their limits. Most CBA-like methods actually are One-Vs-All approaches (OVA), i.e., the selected classification rules are relevant for one class and irrelevant for the union of the other classes. In this paper, we point out recurrent problems encountered by OVA approaches applied to multi-class imbalanced data sets (e.g., improper bias towards majority classes, conflicting rules). That is why we propose a new One-Versus-Each (OVE) framework. In this framework, a rule has to be relevant for one class and irrelevant for every other class taken separately. Our approach, called fitcare, is empirically validated on various benchmark data sets and our theoretical findings are confirmed.