A noise-detection based AdaBoost algorithm for mislabeled data

Noise sensitivity is known as a key related issue of AdaBoost algorithm. Previous works exhibit that AdaBoost is prone to be overfitting in dealing with the noisy data sets due to its consistent high weights assignment on hard-to-learn instances (mislabeled instances or outliers). In this paper, a n...

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Vydáno v:Pattern recognition Ročník 45; číslo 12; s. 4451 - 4465
Hlavní autoři: Cao, Jingjing, Kwong, Sam, Wang, Ran
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.12.2012
Elsevier
Témata:
AdaBoost
Algorithms
Applied sciences
Dealing
Ensemble learning
Error analysis
Exact sciences and technology
Information, signal and communications theory
k-NN
Maximization
Pattern recognition
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Training
EM
AdaBoost
k-NN
Pattern recognition
Ensemble learning
Nearest neighbour
Automatic classification
Resource allocation
Iterative method
Signal classification
k-N
Learning
Outlier
Loss function
Resource management
EM algorithm
Learning algorithm
ISSN:0031-3203, 1873-5142
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Shrnutí:Noise sensitivity is known as a key related issue of AdaBoost algorithm. Previous works exhibit that AdaBoost is prone to be overfitting in dealing with the noisy data sets due to its consistent high weights assignment on hard-to-learn instances (mislabeled instances or outliers). In this paper, a new boosting approach, named noise-detection based AdaBoost (ND-AdaBoost), is exploited to combine classifiers by emphasizing on training misclassified noisy instances and correctly classified non-noisy instances. Specifically, the algorithm is designed by integrating a noise-detection based loss function into AdaBoost to adjust the weight distribution at each iteration. A k-nearest-neighbor (k-NN) and an expectation maximization (EM) based evaluation criteria are both constructed to detect noisy instances. Further, a regeneration condition is presented and analyzed to control the ensemble training error bound of the proposed algorithm which provides theoretical support. Finally, we conduct some experiments on selected binary UCI benchmark data sets and demonstrate that the proposed algorithm is more robust than standard and other types of AdaBoost for noisy data sets. ► A noise-detection based loss function is proposed to distinguish mislabeled data from misclassified instances. ► A new regeneration condition is added to guarantee the generalization performance of the proposed algorithm. ► The proposed methods significantly outperform most of the state-of-the-art methods in high noise region.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2012.05.002