A Survey on Multi-Label Data Stream Classification

Xiu-lin, Zheng; Li, Peipei; Zhang, Chu; Hu, Xuegang

doi:10.1109/access.2019.2962059

Cited by 56 publications

(28 citation statements)

References 110 publications

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“…Relying on the above considerations and several CNN architectures reported in other research [ 16 , 17 ], we deployed Inception v3 [ 18 ] of GoogLeNet as a feature extraction network in FAR-Net. Inception v3 is an optimized version proposed by the Google team based on Inception v1.…”

Section: Feature-wise Attention-based Relation Networkmentioning

confidence: 99%

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification

Zheng

You

et al. 2021

Sensors

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In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.

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Section: Feature-wise Attention-based Relation Networkmentioning

confidence: 99%

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification

Zheng

You

et al. 2021

Sensors

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“…In [17], traditional multi-label classification tasks and their performance comparison analyzed in various aspects are well summarized. [18] has achieved meaningful performance using Non-dominated Sorting Genetic Algorithm [19] to extract optimal subset features from multi-label video data.…”

Section: Related Workmentioning

confidence: 99%

Learning Individual Class Representation From Biased Multi-Label Data

Tserendorj

Lee

2021

IEEE Access

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Image recognition is a popular and important research field of computer vision. Recently with the development of deep learning technology, image recognition performance has been improved significantly. However with multi-label images, recognizing individual category is a challenging task. In order to address the problem, we propose a Feature Disintegrator (FD) that decomposes co-occurred instance features of multi-label into individual categories. In experimental evaluation, proposed method achieves the gains of mean average precision (mAP) up to 18.67% and 29.05% over baseline networks in ML-MNIST and ML-CIFAR, respectively. It shows 5.76% and 6.65% higher mAP than baseline on PASCAL VOC-2007 and MS-COCO data set respectively.

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“…The preprocessing stage in many research papers are based on traditional machine learning algorithms that use single label data [31]. Different classification methods are enhanced to be adapted with more than one class label.…”

Section: B Data Preprocessingmentioning

confidence: 99%

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

et al. 2020

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Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members' to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members' devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes' assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students' learning experiences.

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A Survey on Multi-Label Data Stream Classification

Cited by 56 publications

References 110 publications

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification

Learning Individual Class Representation From Biased Multi-Label Data

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

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