Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data

Zhang, Yifan; Zhao, Peilin; Cao, Jiezhang; Ma, Wenye; Huang, Junzhou; Wu, Qingyao; Tan, Mingkui

doi:10.1145/3219819.3219948

Cited by 34 publications

(18 citation statements)

References 24 publications

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“…In the papers [11,12] the authors study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data are from offline sources and can be easily annotated; the authors propose some techniques that exploit offline knowledge transfering it a online different domain. Other papers [13,14] deal with the problems of limited query budget (when it is difficult to annotated date) and the problem of highly imbalance ratio between classes.…”

Section: Online Machine Learning Methodsmentioning

confidence: 99%

Interactive online learning for graph matching using active strategies

Conte

Serratosa

2020

Knowledge-Based Systems

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In some pattern recognition applications, objects are represented by attributed graphs, in which nodes represent local parts of the objects and edges represent relationships between these local parts. In this framework, the comparison between objects is performed through the distance between attributed graphs. Usually, this distance is a linear equation defined by some cost functions on the nodes and on the edges of both attributed graphs. In this paper, we present an online, active and interactive method for learning these cost functions, which works as follows. Graphs are provided to the learning algorithm by pairs in a sequential order (online). Then, a correspondence between them is computed,

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Section: Online Machine Learning Methodsmentioning

confidence: 99%

Interactive online learning for graph matching using active strategies

Conte

Serratosa

2020

Knowledge-Based Systems

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“…A brief version of this paper had been published in SIGKDD conference [40]. Compared with it, this journal manuscript makes several significant extensions, including (1) two updated variants with sketching methods, and some theoretical analyses about their time complexity; (2) more empirical studies to evaluate the proposed algorithms.…”

Section: Section D Related Workmentioning

confidence: 99%

Online Adaptive Asymmetric Active Learning With Limited Budgets

Zhang

Zhao

Niu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced. In practice, it is quite difficult to handle imbalanced unlabeled datastream when only a limited budget of labels can be queried for training. To solve this, previous OAL studies adopt either asymmetric losses or queries (an isolated asymmetric strategy) to tackle the imbalance, and use first-order methods to optimize the cost-sensitive measure. However, the isolated strategy limits their performance in class imbalance, while first-order methods restrict their optimization performance. In this paper, we propose a novel Online Adaptive Asymmetric Active learning algorithm, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization. We theoretically analyze its mistake bound and cost-sensitive metric bounds. Moreover, to better balance performance and efficiency, we enhance our algorithm via a sketching technique, which significantly accelerates the computational speed with quite slight performance degradation. Promising results demonstrate the effectiveness and efficiency of the proposed methods.

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“…Furthermore, there are some other algorithm-level solutions, such as one-class learning [16,17,18,19], active learning [20,21,22,23] and imbalance learning algorithm based on extreme learning machines [24,25,26]. proposed [3], which can be divided into three categories:…”

Section: Ir = # # ⁄mentioning

confidence: 99%

Under-Sampling and Feature Selection Algorithms for S2SMLP

Liu

Zhang

2020

IEEE Access

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Imbalance learning is a hot topic in the data mining and machine learning domains. Data-level, algorithm-level and ensemble solutions are the three main methods proposed thus far to address imbalance learning. To alleviate the issues of data explosion and feature selection in multilayer perceptron based on simultaneous two-sample representation(S2SMLP), in this paper, firstly, spectral clustering is exploited to select majority samples so as to construct a smaller training dataset for the classifier. We divide all majority samples into many clusters through spectral clustering, extract different numbers of representative samples from a cluster according to the size of each cluster, the average distance between the minority class and all samples of the cluster, then construct the training dataset of the classifier by combining these extracted samples from the majority class and all minority samples. Secondly, we propose a novel feature selection method based on the pairwise samples distance constraint, which considers the class labels of paired samples, select the features which push two similar samples closer together and pull two different samples farther apart. Finally, we conduct extensive experiments on 44 two-class imbalanced datasets and four highdimensional DNA microarray datasets. The experimental results demonstrate that our proposed algorithms outperform some state-of-the-art algorithms in terms of F-measure,-mean and AUC. INDEX TERMS Multilayer perceptron, Under-sampling, Spectral clustering, Imbalance learning, feature selection, information gain I. INTRODUCTION Classification is one of the hot topics of machine learning domains, its main task is to learn a classification model from training data and predict the labels of unknown samples. To date, many classification models have been proposed and are widely used in various real-world applications, e.g., naive Bayes (NB), logistic regression (LR), support vector machine (SVM) have been successfully employed in spam recognition, bank loan credit scoring and network rumor recognition, respectively. The success of traditional classification model is usually supported by the assumption that different classes in an original dataset are balanced [1], but the assumption is not always true. In other words, there is a class-imbalanced problem in a dataset [2], one class contains a large number of samples, which is called the majority class (or negative class), and the other class has a very small number of samples, which is called the minority class (or positive class), and IR is the imbalance ratio defined as follows.

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Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data

Cited by 34 publications

References 24 publications

Interactive online learning for graph matching using active strategies

Interactive online learning for graph matching using active strategies

Online Adaptive Asymmetric Active Learning With Limited Budgets

Under-Sampling and Feature Selection Algorithms for S2SMLP

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