C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Top-down induction of first-order logical decision trees
Artificial Intelligence
REFACING: An autonomic approach to network security based on multidimensional trustworthiness
Computer Networks: The International Journal of Computer and Telecommunications Networking
Class Noise Mitigation Through Instance Weighting
ECML '07 Proceedings of the 18th European conference on Machine Learning
Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction
Advanced Web and NetworkTechnologies, and Applications
Domain independent data discrepancy detection using ensemble learning
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Genre-based decomposition of email class noise
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Use of Classification Algorithms in Noise Detection and Elimination
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Toward breast cancer survivability prediction models through improving training space
Expert Systems with Applications: An International Journal
An industrial case study of classifier ensembles for locating software defects
Software Quality Control
Boosting parallel perceptrons for label noise reduction in classification problems
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
On the use of data filtering techniques for credit risk prediction with instance-based models
Expert Systems with Applications: An International Journal
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
Ensemble-based noise detection: noise ranking and visual performance evaluation
Data Mining and Knowledge Discovery
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Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more accurate than any of its component classifiers. In this paper, we use ensemble methods to identify noisy training examples. More precisely, we consider the problem of mislabeled training examples in classification tasks, and address this problem by pre-processing the training set, i.e. by identifying and removing outliers from the training set. We study a number of filter techniques that are based on well-known ensemble methods like cross-validated committees, bagging and boosting. We evaluate these techniques in an Inductive Logic Programming setting and use a first order decision tree algorithm to construct the ensembles.