C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Machine Learning
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
An industrial case study of classifier ensembles for locating software defects
Software Quality Control
Ensemble-based noise detection: noise ranking and visual performance evaluation
Data Mining and Knowledge Discovery
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Due to the imprecise nature of biological experiments, biological data are often characterized by the presence of redundant and noisy data, which are usually derived from errors associated with data collection, such as contaminations in laboratorial samples. Gene expression data represent an example of noisy biological data that suffer from this problem. Machine Learning algorithms have been successfully used in gene expression analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from data can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques in gene expression data, analyzing the effectiveness of these techniques and combinations of them in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data. The results obtained indicate that the pre-processing techniques employed were effective for noise detection.