Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
The impact of poor data quality on the typical enterprise
Communications of the ACM
Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms
Computational Statistics & Data Analysis
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Robustness against separation and outliers in logistic regression
Computational Statistics & Data Analysis
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Computing LTS Regression for Large Data Sets
Data Mining and Knowledge Discovery
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Outlier identification in high dimensions
Computational Statistics & Data Analysis
Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data
Computational Statistics & Data Analysis
Robust PCA for skewed data and its outlier map
Computational Statistics & Data Analysis
ACM Computing Surveys (CSUR)
Robust least squares support vector machine based on recursive outlier elimination
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Expert Systems with Applications: An International Journal
Weighted principal component analysis
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
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Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation.