C4.5: programs for machine learning
C4.5: programs for machine learning
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
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Approximation of Frequency Queris by Means of Free-Sets
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Probabilistic Noise Identification and Data Cleaning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Dealing with predictive-but-unpredictable attributes in noisy data sources
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
World Wide Web
Computing the minimum-support for mining frequent patterns
Knowledge and Information Systems
Class Noise Mitigation Through Instance Weighting
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Parameter-Free Associative Classification Method
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Noise Modeling with Associative Corruption Rules
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Supporting bi-cluster interpretation in 0/1 data by means of local patterns
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Feature construction based on closedness properties is not that simple
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Feature construction and δ-free sets in 0/1 samples
DS'06 Proceedings of the 9th international conference on Discovery Science
Optimized rule mining through a unified framework for interestingness measures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on "non-class" attribute noise and we consider how a frequent fault-tolerant (FFT) pattern mining task can be used to support noise-tolerant classification. Our method is based on an application independent strategy for feature construction based on the so-called *** -free patterns. Our experiments on noisy training data shows accuracy improvement when using the computed features instead of the original ones.