A new margin-based AdaBoost algorithm: Even more robust than RobustBoost to class-label noise

2016 ◽  
Author(s):  
Omid Ranjbar Pouya
Keyword(s):  
Class Label ◽  
Label Noise ◽  
Adaboost Algorithm

A two-stage ensemble method for the detection of class-label noise

2018 ◽  
Vol 275 ◽  
pp. 2374-2383 ◽  
Author(s):  
Maryam Sabzevari ◽  
Gonzalo Martínez-Muñoz ◽  
Alberto Suárez
Keyword(s):  
Ensemble Method ◽  
Class Label ◽  
Two Stage ◽  
Label Noise

scholarly journals Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

2017 ◽  
Vol 9 (2) ◽  
pp. 173 ◽  
Author(s):  
Charlotte Pelletier ◽  
Silvia Valero ◽  
Jordi Inglada ◽  
Nicolas Champion ◽  
Claire Marais Sicre ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Mapping ◽  
Class Label ◽  
Label Noise ◽  
Training Class

scholarly journals Label Noise Detection System Against Label Flipping Attack

2021 ◽  
Author(s):  
Ning Cheng ◽  
Hongpo Zhang ◽  
Zhanbo Li
Keyword(s):  
Detection System ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Correction Algorithm ◽  
Label Noise ◽  
Adaboost Algorithm ◽  
Learning Techniques ◽  
Noise Ratio ◽  
Noise Correction ◽  
Better Than

Abstract The label flipping attack is a special poisoning attack in the adversarial environment. By adding noise to the data, it destroys the learning process of the model and affects the decision-making performance of the model. Recent literature work uses semi-supervised learning techniques to defend against label flipping attacks. However, these methods require a clean dataset to achieve their goals. This study proposes a novel label noise processing framework to correct the labels of contaminated samples in the data preprocessing stage. Based on five real UCI datasets, we evaluated the effectiveness of the semi-supervised defensive label noise correction algorithm based on the AdaBoost algorithm(AdaSSL). With a noise ratio of 0~20%, we compared the classification performance of six classic machine learning algorithms (NB, LR, SVM, DT, KNN and MLP) under the AdaSSL defense algorithm. The results show that compared with the most advanced semi-supervised defense algorithms in the literature, our algorithm does not need to use an additional dataset. At a noise ratio of 10%, the AdaSSL algorithm is significantly better than state of the art label noise defense technology.

The Effects of Class Label Noise on Highly-Imbalanced Big Data

2021 ◽  
Author(s):  
Robert K. L. Kennedy ◽  
Justin M. Johnson ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Big Data ◽  
Class Label ◽  
Label Noise

scholarly journals Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Shehzad Khalid ◽  
Sannia Arshad ◽  
Sohail Jabbar ◽  
Seungmin Rho
Keyword(s):  
Real Life ◽  
Weight Vector ◽  
Training Data ◽  
Class Label ◽  
Label Noise ◽  
Ensemble Techniques ◽  
Classification Framework ◽  
Class Level ◽  
Free Data ◽  
Weight Learning

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension ofm-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

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