Enhancing Adaboost performance in the presence of class-label noise: A comparative study on EEG-based classification of schizophrenic patients and benchmark datasets

The performance of Adaboost is highly sensitive to noisy and outlier samples. This is therefore the weights of these samples are exponentially increased in successive rounds. In this paper, three novel schemes are proposed to hunt the corrupted samples and eliminate them through the training process...

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Bibliographic Details
Published inIntelligent data analysis Vol. 28; no. 1; pp. 357 - 376
Main Authors Pouya, Omid Ranjbar, Boostani, Reza, Sabeti, Malihe
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.01.2024
Sage Publications Ltd
Subjects
Benchmarks
Cluster analysis
Clustering
Comparative studies
Datasets
Electroencephalography
Fractal geometry
Labels
Outliers (statistics)
Support vector machines
Vector quantization
Class-label noise
AdaBoost
support vector machines
noise pruning
margin theory
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Summary:The performance of Adaboost is highly sensitive to noisy and outlier samples. This is therefore the weights of these samples are exponentially increased in successive rounds. In this paper, three novel schemes are proposed to hunt the corrupted samples and eliminate them through the training process. The methods are: I) a hybrid method based on K-means clustering and K-nearest neighbor, II) a two-layer Adaboost, and III) soft margin support vector machines. All of these solutions are compared to the standard Adaboost on thirteen Gunnar Raetsch’s datasets under three levels of class-label noise. To test the proposed method on a real application, electroencephalography (EEG) signals of 20 schizophrenic patients and 20 age-matched control subjects, are recorded via 20 channels in the idle state. Several features including autoregressive coefficients, band power and fractal dimension are extracted from EEG signals of all participants. Sequential feature subset selection technique is adopted to select the discriminative EEG features. Experimental results imply that exploiting the proposed hunting techniques enhance the Adaboost performance as well as alleviating its robustness against unconfident and noisy samples over Raetsch benchmark and EEG features of the two groups.
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ISSN:1088-467X
1571-4128
DOI:10.3233/IDA-227125