An Active Learning Algorithm Based on Shannon Entropy for Constraint-Based Clustering

Chen, Duo Wen; Jin, Ying

doi:10.1109/access.2020.3025036

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Other improved methods, such as the semisupervised clustering algorithm based on pairwise constraints, can enhance clustering performance. It advocates the use of prior knowledge as pairwise constraints to enable the clustering algorithm to obtain abundant heuristic information and reduce blindness in the search process [14,15]. However, this type of algorithm has two main problems: it is unsure whether a solution that satisfies all constraints exists, and it relies too much on prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Novel Automated K-means++ Algorithm for Financial Data Sets

Zhang

et al. 2021

Mathematical Problems in Engineering

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The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.

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Section: Introductionmentioning

confidence: 99%

Novel Automated K-means++ Algorithm for Financial Data Sets

Zhang

et al. 2021

Mathematical Problems in Engineering

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“…Due to the limited number of locations that can query, it is possible that some important locations are not selected, and the information from those locations will not be learned by the model. Information entropy-based approach [49] and attention-based mechanism [50] are able to address this issue and will be tested in future works.…”

Section: Discussionmentioning

confidence: 99%

Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems

et al. 2022

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Spatio-temporal forecasting is of great importance in a wide range of dynamical systems applications, such as earth science, transport planning, etc. These applications rely on accurate predictions of spatio-temporal structured data reflecting real-world phenomena. A stunning characteristic is that the dynamical system is not only driven by some physics laws but also impacted by the localized factor in spatial and temporal regions. One of the major challenges is to infer the underlying causes, which generate the perceived data stream and propagate the involved causal dynamics through the distributed observing units. Another challenge is that the success of machine learning based predictive models requires massive annotated data for model training. However, the acquisition of high-quality annotated data is objectively manual and tedious as it needs a considerable amount of human intervention, making it infeasible in fields that require high levels of expertise. To tackle these challenges, we advocate a spatio-temporal physicscoupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics. To deal with data-acquisition constraints, an active learning mechanism with Kriging for actively acquiring the most informative data is proposed for ST-PCNN training in a partially observable environment. Our experiments on both synthetic and real-world datasets exhibit that the proposed ST-PCNN with active learning converges to near optimal accuracy with substantially fewer instances.

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“…The information entropy is used to measure the amount of information in a sample [22][23][24]. The smaller the information entropy, the less category information the samples contain, and vice versa.…”

Section: Sample Confidence Measurementmentioning

confidence: 99%

Motor Imagery Recognition Method Based on Multisource Transfer Learning and Multiclassifier Fusion

Gao

Sun

2022

Wireless Communications and Mobile Computing

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There are two common problems in the field of motor imagery (MI) recognition, which are poor generalization and low recognition performance. A recognition method based on multisource transfer learning and multiclassifier fusion is therefore proposed to realize the MI classification. In this approach, multisource transfer learning method is used to transfer samples from multiple source domains to target domain. The source domain selection method based on distribution similarity is designed to select those source domains whose distribution is similar to the target domain, and samples with high information entropy are selected from these source domains for transferring. Then, an MI classification method is proposed through the fusion of multiple classifiers. The classifiers are trained by labeled samples in the target domain and the transferred samples in multiple source domains. The new sample in the target domain can be identified by the weight fusion of the results of these classifiers. In order to verify the effectiveness of the proposed method, four types of motor imagery in the BCI Competition IV dataset 2a were used to evaluate the recognition ability, and the results approved an excellent recognition and generalization performance as well as a better training efficiency comparing to the well-applied methods nowadays.

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An Active Learning Algorithm Based on Shannon Entropy for Constraint-Based Clustering

Cited by 5 publications

References 30 publications

Novel Automated K-means++ Algorithm for Financial Data Sets

Novel Automated K-means++ Algorithm for Financial Data Sets

Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems

Motor Imagery Recognition Method Based on Multisource Transfer Learning and Multiclassifier Fusion

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