Learning kernel logistic regression in the presence of class label noise

Bootkrajang, Jakramate; Kabán, Ata

doi:10.1016/j.patcog.2014.05.007

Cited by 46 publications

(17 citation statements)

References 36 publications

(40 reference statements)

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“…Compared to our method, the defense method presented in [6] cannot correctly label the poisoning samples while our defense methods imposes the model to tackle such data points and relabeling them. The work in [10] focus on building an automatic robust multiple kernel-based logistic regression classifier against poisoning attacks without applying any cross-validation. Despite the fact that proposed classifier may improve performance and learning speed; it does suffer from lack of any theoretical guarantees.…”

Section: Defense Algorithms Against Poisoning Attacksmentioning

confidence: 99%

On defending against label flipping attacks on malware detection systems

Taheri

Javidan

Shojafar

et al. 2020

Neural Comput & Applic

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Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-Nearest Neighboring (KNN) algorithm to defend against such attacks. However, such an approach can suffer from low to wrong detection accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on Silhouette clustering method, modified for mobile Android platforms. We proposed two Convolutional Neural Network (CNN)-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack (SCLFA). We show the effectiveness of these two defense algorithms -Label-based Semi-supervised Defense (LSD) and clustering-based Semi-supervised Defense (CSD) -in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature.

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Section: Defense Algorithms Against Poisoning Attacksmentioning

confidence: 99%

On defending against label flipping attacks on malware detection systems

Taheri

Javidan

Shojafar

et al. 2020

Neural Comput & Applic

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“…Meanwhile, we minimize the bias between the source domain and the target domain by minimizing the Type II error. While multi-kernel method has been widely discussed [23,24,20] and used [21], our work demonstrates that the kernel choice is pivotal to cross-domain learning.…”

Section: Introductionmentioning

confidence: 94%

Optimal kernel choice for domain adaption learning

Dong

Feng

Quan

et al. 2016

Engineering Applications of Artificial Intelligence

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In this paper, a kernel choice method is proposed for domain adaption, referred to as Optimal Kernel Choice Domain Adaption (OKCDA). It learns a robust classier and parameters associate with Multiple Kernel Learning side by side. Domain adaption kernel-based learning strategy has shown outstanding performance. It embeds two domains of different distributions, namely, the auxiliary and the target domains, into Hilbert Space, and exploits the labeled data from the source domain to train a robust kernel-based SVM classier for the target domain. We reduce the distributions mismatch by setting up a test statistic between the two domains based on the Maximum Mean Discrepancy (MMD) algorithm and minimize the Type II error, given an upper bound on error I. Simultaneously, we minimize the structural risk functional. In order to highlight the advantages of the proposed method, we tackle a text classication problem on 20 Newsgroups dataset and Email Spam dataset. The results demonstrate that our method exhibits outstanding performance.

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“…Deep learning algorithms can be extended to handle label noise by additional network layers for noise modeling [45]. Robust kernels can be learned from the data in order to improve the effectiveness of kernel-based methods under label noise [7] and robust SVM methods have also been considered [44] [6].…”

Section: Related Workmentioning

confidence: 99%

Hubness-aware kNN classification of high-dimensional data in presence of label noise

Tomašev

Búza

2015

Neurocomputing

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Learning with label noise is an important issue in classification, since it is not always possible to obtain reliable data labels. In this paper we explore and evaluate a new approach to learning with label noise in intrinsically high-dimensional data, based on using neighbor occurrence models for hubness-aware k-nearest neighbor classification. Hubness is an important aspect of the curse of dimensionality that has a negative effect on many types of similarity-based learning methods. As we will show, the emergence of hubs as centers of influence in high-dimensional data affects the learning process in presence of label noise. We evaluate the potential impact of hub-centered noise by defining a hubness-proportional random label noise model that is shown to induce a significantly higher kNN misclassification rate than the uniform random label noise. Real-world examples are discussed where hubness-correlated noise arises either naturally or as a consequence of an adversarial attack. Our experimental evaluation reveals that hubness-based fuzzy k-nearest neighbor classification and Naive Hubness-Bayesian k-nearest neighbor classification might be suitable for learning under label noise in intrinsically high-dimensional data, as they exhibit robustness to high levels of random label noise and hubness-proportional random label noise. The results demonstrate promising performance across several data domains.

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Learning kernel logistic regression in the presence of class label noise

Cited by 46 publications

References 36 publications

On defending against label flipping attacks on malware detection systems

On defending against label flipping attacks on malware detection systems

Optimal kernel choice for domain adaption learning

Hubness-aware kNN classification of high-dimensional data in presence of label noise

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