Abstract
Discovering noisy data and classification of noisy data sets are problematic issues associated with noisy data sets. In our work, we used 36 UCI data sets that consist of differeent rates of noisy data to measure robustness of five ensemble learners and two basic classifiers to noisy data. According to classification success ratesof our study, Random Subspace and Bagging are more robust to noisy data than other ensemble learners and simple classifiers.
Keywords
Robustness (evolution)
Noisy data
Computer science
Subspace topology
Random subspace method
Artificial intelligence
Ensemble learning
Random forest
Pattern recognition (psychology)
Machine learning
Data mining
Data modeling
Noise measurement
Noise reduction
Subject Areas
Advanced Statistical Methods and Models
·Statistics and Probability ·Physical Sciences
Machine Learning and Data Classification
·Artificial Intelligence ·Physical Sciences
Imbalanced Data Classification Techniques
·Artificial Intelligence ·Physical Sciences
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