Active Learning Using Hint Information

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hung‐Mo

doi:10.1162/neco_a_00755

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…However, due to its inherent characteristics, its effectiveness in practical applications is not very satisfactory. Inspired by the literature [30][31][32], this paper proposes a new semisupervised support vector machine method based on AL techniques, which can combine the advantages of these two algorithms to overcome the defects of TSVM and identify the samples that have the greatest impacts on classifier performance, meanwhile, significantly reducing the burden of users' annotation task.…”

Section: Al (Active Learningmentioning

confidence: 99%

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

Wang

Dai

et al. 2020

Discrete Dynamics in Nature and Society

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In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

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Section: Al (Active Learningmentioning

confidence: 99%

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

Wang

Dai

et al. 2020

Discrete Dynamics in Nature and Society

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“…On the contrary, random sampling only does pure exploration and fails to consider the representativeness of unlabeled samples. One work (Li et al, 2015) also proposed to use some randomly selected samples to explore. However, that technique is particularly designed for binary classification tasks and it is unclear how to extend it to multi-class classification.…”

Section: Comparisons and Connectionsmentioning

confidence: 99%

Single shot active learning using pseudo annotators

Yang

Loog

2019

Pattern Recognition

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Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.

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“…A software defect prediction model can be constructed quickly. Some scholars have proposed active learning strategies that consider informative and representative examples [3][4]. There have been many studies focusing on active learning in the field of within-project defect prediction [5][6][7], Lu et al [8][9] tried to apply active learning to cross-project defect prediction from the perspective of dimensional reduction.…”

Section: Introductionmentioning

confidence: 99%

Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction

Qu¹,

Chen²,

Chen³

et al. 2019

Int J Performability Eng

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In the process of software defect prediction dataset construction, there are problems such as high labeling costs. Active learning can reduce labeling costs when using uncertainty sampling. Samples with the most uncertainty will be labeled, but samples with the highest certainty will always be discarded. According to cognitive theory, easy samples can promote the performance of the model. Therefore, a hybrid active learning query strategy is proposed. For the sample with lowest information entropy, query-by-committee will analyze it again using vote entropy. Empirical studies show that the proposed HIVE approach outperforms several state-of-the-art active learning approaches.

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Active Learning Using Hint Information

Cited by 15 publications

References 18 publications

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

Single shot active learning using pseudo annotators

Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction

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