Active Multi-label Learning with Optimal Label Subset Selection

Jiao, Yang; Wu, Jian; Xian, Xuefeng; Xu, Haihui; Cui, Zhiming

doi:10.1007/978-3-319-14717-8_41

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Mixed‐mode based methods [20, 21] are balanced strategies that take both example space and label space into account. Such methods are employed to select the most informative examples for use in the initial candidate set.…”

Section: Introductionmentioning

confidence: 99%

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Active learning with label correlation exploration for multi‐label image classification

Wang

Sheng³

et al. 2017

IET Computer Vision

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Multi-label image classification has attracted considerable attention in machine learning recently. Active learning is widely used in multi-label learning because it can effectively reduce the human annotation workload required to construct highperformance classifiers. However, annotation by experts is costly, especially when the number of labels in a dataset is large. Inspired by the idea of semi-supervised learning, in this study, the authors propose a novel, semi-supervised multi-label active learning (SSMAL) method that combines automated annotation with human annotation to reduce the annotation workload associated with the active learning process. In SSMAL, they capture three aspects of potentially useful informationclassification prediction information, label correlation information, and example spatial information-and they use this information to develop an effective strategy for automated annotation of selected unlabelled example-label pairs. The experimental results obtained in this study demonstrate the effectiveness of the authors' proposed approach.

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Section: Introductionmentioning

confidence: 99%

“…Specific labels are selected for each candidate example. Jiao et al [20] proposed a method for max‐margin multi‐label active learning with label‐set push, which is a typical mixed‐mode based method. This method involves first selecting a batch of the most uncertain examples, based on the max‐margin algorithm.…”

Section: Introductionmentioning

confidence: 99%

Active learning with label correlation exploration for multi‐label image classification

Wang

Sheng³

et al. 2017

IET Computer Vision

Self Cite

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Active learning for hierarchical multi-label classification

Nakano

Cerri

Vens

2020

Data Min Knowl Disc

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Due to technological advances, a massive amount of data is produced daily, presenting challenges for application areas where data needs to be labelled by a domain specialist or by expensive procedures, in order to be useful for supervised machine learning purposes. In order to select which data points will provide more information when labelled, one can make use of active learning methods. Active learning (AL) is a subfield of machine learning which addresses methods to build models with fewer, but more representative instances. Even though AL has been vastly studied, it has not been thoroughly investigated in hierarchical multi-label classification (HMC), a learning task where multiple class labels can be assigned to an instance and these labels are hierarchically structured. In this work, we provide a public framework containing baseline and state-of-the-art algorithms suitable for this task. Additionally, we also propose a new algorithm, namely Hierarchical Query-By-Committee (H-QBC), which is validated on datasets from different domains. Our results show that H-QBC is capable of providing superior predictive performance results compared to its competitors, while being computationally efficient and parameter free.

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Multi-Label Active Learning Algorithms for Image Classification

Sheng

Zhang

et al. 2020

ACM Comput. Surv.

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Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension.

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Active Multi-label Learning with Optimal Label Subset Selection

Cited by 3 publications

References 15 publications

Active learning with label correlation exploration for multi‐label image classification

Active learning with label correlation exploration for multi‐label image classification

Active learning for hierarchical multi-label classification

Multi-Label Active Learning Algorithms for Image Classification

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