• Corpus ID: 7842019

A Comparison of Outlier Detection Algorithms for Machine Learning

@inproceedings{Escalante2005ACO,
  title={A Comparison of Outlier Detection Algorithms for Machine Learning},
  author={Hugo Jair Escalante},
  year={2005}
}
In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of stellar populations parameters as well as on machine learning benchmark data, inserting artificial noise and outliers. We used kernel principal component analysis in order to reduce the dimensionality of the spectral data. Experiments on noisy and noiseless data were… 

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References

SHOWING 1-10 OF 41 REFERENCES

Algorithms for Mining Distance-Based Outliers in Large Datasets

This paper provides formal and empirical evidence showing the usefulness of DB-outliers and presents two simple algorithms for computing such outliers, both having a complexity of O(k N’), k being the dimensionality and N being the number of objects in the dataset.

A Unified Notion of Outliers: Properties and Computation

A unified outlier detection system can replace a whole spectrum of statistical discordancy tests with a single module detecting only the kinds of outliers proposed.

Robust Decision Trees: Removing Outliers from Databases

This paper examines C4.5, a decision tree algorithm that is already quite robust - few algorithms have been shown to consistently achieve higher accuracy, and extends the pruning method to fully remove the effect of outliers, and this results in improvement on many databases.

Efficient algorithms for mining outliers from large data sets

A novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor is proposed and the top n points in this ranking are declared to be outliers.

An introduction to kernel-based learning algorithms

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.

Noise Clustering with a Fixed Fraction of Noise

The so-called noise clustering technique is modified making it more robust against a wrong choice of its main control parameter, the noise distance, including a computationally efficient algorithm.

Discovering Informative Patterns and Data Cleaning

A method for discovering informative patterns from data that can be reduced to only a few representative data entries and an attractive candidate for new applications in knowledge discovery is presented.

Probabilistic noise identification and data cleaning

  • J. KubicaA. Moore
  • Computer Science
    Third IEEE International Conference on Data Mining
  • 2003
This work presents LENS, an approach for identifying corrupted fields and using the remaining noncorrupted fields for subsequent modeling and analysis, and provides an algorithm for the unsupervised discovery of such models.

Identifying and Eliminating Mislabeled Training Instances

Empirical results suggest that the ensemble filter approach is an effective method for identifying labeling errors, and further, that the approach will significantly benefit ongoing research to develop accurate and robust remote sensing-based methods to map land cover at global scales.

Nonlinear Component Analysis as a Kernel Eigenvalue Problem

A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.