Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> F. Ensemble Methods <s> Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization. <s> BIB001 </s> Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> F. Ensemble Methods <s> Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. In this paper, multiple TAN classifiers generated by GTAN are combined by a combination method called Boosting-MultiTAN is compared with the Boosting-BAN classifier which is boosting based on BAN combination. We conduct an empirical study to compare the performance of two algorithms, measured in terms of overall test correct rate, on ten real data sets. Finally, experimental results show that the Boosting-BAN has higher classification accuracy on most data sets, but Boosting-MultiTAN has good effect on others. These results argue that boosting algorithms deserve more attention in machine learning and data mining communities. <s> BIB002 </s> Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> F. Ensemble Methods <s> 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. <s> BIB003 </s> Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> F. Ensemble Methods <s> Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware AdaBoost, is still susceptible to label noise. However, pairing it with the new robust Boosting algorithm we propose here results in a more resilient algorithm under mislabelling. <s> BIB004 </s> Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> F. Ensemble Methods <s> AdaBoost has attracted much attention in the machine learning community because of its excellent performance in combining weak classifiers into strong classifiers. However, AdaBoost tends to overfit to the noisy data in many applications. Accordingly, improving the antinoise ability of AdaBoost plays an important role in many applications. The sensitiveness to the noisy data of AdaBoost stems from the exponential loss function, which puts unrestricted penalties to the misclassified samples with very large margins. In this paper, we propose two boosting algorithms, referred to as RBoost1 and RBoost2, which are more robust to the noisy data compared with AdaBoost. RBoost1 and RBoost2 optimize a nonconvex loss function of the classification margin. Because the penalties to the misclassified samples are restricted to an amount less than one, RBoost1 and RBoost2 do not overfocus on the samples that are always misclassified by the previous base learners. Besides the loss function, at each boosting iteration, RBoost1 and RBoost2 use numerically stable ways to compute the base learners. These two improvements contribute to the robustness of the proposed algorithms to the noisy training and testing samples. Experimental results on the synthetic Gaussian data set, the UCI data sets, and a real malware behavior data set illustrate that the proposed RBoost1 and RBoost2 algorithms perform better when the training data sets contain noisy data. <s> BIB005 </s> Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey <s> F. Ensemble Methods <s> AdaBoost has been theoretically and empirically proved to be a very successful ensemble learning algorithm, which iteratively generates a set of diverse weak learners and combines their outputs using the weighted majority voting rule as the final decision. However, in some cases, AdaBoost leads to overfitting especially for mislabeled noisy training examples, resulting in both its degraded generalization performance and non-robustness. Recently, a representative approach named noise-detection based AdaBoost (ND_AdaBoost) has been proposed to improve the robustness of AdaBoost in the two-class classification scenario, however, in the multi-class scenario, this approach can hardly achieve satisfactory performance due to the following three reasons. (1) If we decompose a multi-class classification problem using such strategies as one-versus-all or one-versus-one, the obtained two-class problems usually have imbalanced training sets, which negatively influences the performance of ND_AdaBoost. (2) If we directly apply ND_AdaBoost to the multi-class classification scenario, its two-class loss function is no longer applicable and its accuracy requirement for the (weak) base classifiers, i.e., greater than 0.5, is too strong to be almost satisfied. (3) ND_AdaBoost still has the tendency of overfitting as it increases the weights of correctly classified noisy examples, which could make it focus on learning these noisy examples in the subsequent iterations. To solve the dilemma, in this paper, we propose a robust multi-class AdaBoost algorithm (Rob_MulAda) whose key ingredients consist in a noise-detection based multi-class loss function and a new weight updating scheme. Experimental study indicates that our newly-proposed weight updating scheme is indeed more robust to mislabeled noises than that of ND_AdaBoost in both two-class and multi-class scenarios. In addition, through the comparison experiments, we also verify the effectiveness of Rob_MulAda and provide a suggestion in choosing the most appropriate noise-alleviating approach according to the concrete noise level in practical applications. <s> BIB006
It is well known that bagging is more robust to label noise than boosting BIB001 . Boosting algorithms like AdaBoost puts too much weight on noisy samples, resulting in overfitting the noise. However, the degree of label noise robustness changes for the chosen boosting algorithm, for example it is shown that BrownBoost and LogitBoost is more robust than AdaBoost BIB002 . Therefore noise robust alternatives of AdaBoost is proposed in literature, such as noise detection based AdaBoost BIB003 , rBoost BIB004 , RBoost1&RBoost2 BIB005 and robust multi-class AdaBoost BIB006 .