C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Information Retrieval
Machine Learning
Families of splitting criteria for classification trees
Statistics and Computing
Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Predicting rare classes: can boosting make any weak learner strong?
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ART: A Hybrid Classification Model
Machine Learning
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Coordinated internet attacks: responding to attack complexity
Journal of Computer Security
A Multi-Class SLIPPER System for Intrusion Detection
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Predicting Software Escalations with Maximum ROI
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Information extraction from voicemail transcripts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
World Wide Web
Raising data for improved support in rule mining: How to raise and how far to raise
Intelligent Data Analysis
Techniques for Classifying Executions of Deployed Software to Support Software Engineering Tasks
IEEE Transactions on Software Engineering
Local decomposition for rare class analysis
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
Outlier Detection with Kernel Density Functions
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
ACM Computing Surveys (CSUR)
The Needles-in-Haystack Problem
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Efficient Pruning Schemes for Distance-Based Outlier Detection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
An interface for medical diagnosis support: from the viewpoint of chance discovery
International Journal of Advanced Intelligence Paradigms
COG: local decomposition for rare class analysis
Data Mining and Knowledge Discovery
A study of dynamic meta-learning for failure prediction in large-scale systems
Journal of Parallel and Distributed Computing
Journal of Intelligent Information Systems
Customer Validation of Commercial Predictive Models
Proceedings of the 2010 conference on Data Mining for Business Applications
Frequent subsequence-based protein localization
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Incremental connectivity-based outlier factor algorithm
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
Multi-level relationship outlier detection
International Journal of Business Intelligence and Data Mining
Causal inference with rare events in large-scale time-series data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this paper, we analyze our previously proposed two-phase rule induction method in the context of learning complete and precise signatures of rare classes. The key feature of our method is that it separately conquers the objectives of achieving high recall and high precision for the given target class. The first phase of the method aims for high recall by inducing rules with high support and a reasonable level of accuracy. The second phase then tries to improve the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules. Existing sequential covering techniques try to achieve high precision for each individual disjunct learned. In this paper, we claim that such approach is inadequate for rare classes, because of two problems: splintered false positives and error-prone small disjuncts. Motivated by the strengths of our two-phase design, we design various synthetic data models to identify and analyze the situations in which two state-of-the-art methods, RIPPER and C4.5 rules, either fail to learn a model or learn a very poor model. In all these situations, our two-phase approach learns a model with significantly better recall and precision levels. We also present a comparison of the three methods on a challenging real-life network intrusion detection dataset. Our method is significantly better or comparable to the best competitor in terms of achieving better balance between recall and precision.