Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pruning Algorithms for Rule Learning
Machine Learning
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Conditions for Occam's Razor Applicability and Noise Elimination
ECML '97 Proceedings of the 9th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Orange: from experimental machine learning to interactive data mining
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
The pairwise attribute noise detection algorithm
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Enhancing software quality estimation using ensemble-classifier based noise filtering
Intelligent Data Analysis
Introduction to Information Retrieval
Introduction to Information Retrieval
Use of Classification Algorithms in Noise Detection and Elimination
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A Cluster-Based Noise Detection Algorithm
DBTA '09 Proceedings of the 2009 First International Workshop on Database Technology and Applications
Class noise detection using frequent itemsets
Intelligent Data Analysis
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Ensemble methods for noise elimination in classification problems
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Ensembles of pre-processing techniques for noise detection in gene expression data
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Advances in Class Noise Detection
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Performance Analysis of Class Noise Detection Algorithms
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
ClowdFlows: a cloud based scientific workflow platform
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
| Hi-index | 0.00 |
Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise-Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.