Fuzzy KNN Method With Adaptive Nearest Neighbors

Bian, Zekang; Vong, Chi‐Man; Wong, Pak Kin; Wang, Shitong

doi:10.1109/tcyb.2020.3031610

Cited by 28 publications

(6 citation statements)

References 53 publications

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“…Training is performed with known labels, following which test samples are predicted using the learned model. The training and testing data need not be identical for KNN [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

2022

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Background Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies. Objective This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models. Materials and methods The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence. Results A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76–0.99), I2 = 97% (95% CI 0.96–0.98), p < 0.001. Conclusions Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.

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“…Training is performed with known labels, following which test samples are predicted using the learned model. The training and testing data need not be identical for KNN [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

2022

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“…where 𝑁𝑁(𝑐𝑐 𝑚𝑚 ) denotes the size of class 𝑐𝑐 𝑚𝑚 in the training set, 𝑚𝑚𝑚𝑚𝑥𝑥�𝑁𝑁�𝑐𝑐 𝑗𝑗 �� is the size of the largest class, 𝛼𝛼 is a nonnegative integer used for maintaining a reasonable minimum value of 𝑘𝑘 𝑐𝑐 𝑚𝑚 , 𝐶𝐶 is the number of classes, and 𝑘𝑘 is the original input integer for defining the nearest neighbors. (3) The optimal 𝑘𝑘 value was found for each testing sample [39] or each training sample [40,41] based on the reconstruction framework or other methods. The literature has shown that the reconstruction framework has better accuracy than the other two methods in most cases, but this method is too complex and time consuming.…”

Section: Determination Of the Core And Boundary Components Of Land Co...mentioning

confidence: 99%

Generating Fuzzy Membership Functions for Modeling Wetland Ecosystems From Multispectral Remote Sensing Images

Guo,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The inherent fuzziness of wetland ecosystems largely accounts for the spectral variability of wetland ecosystems in remote sensing images. In addition, a limited spatial resolution leads to the presence of many mixed pixels in middle-resolution multispectral remote sensing images. Existing methods that use a single center or a limited number of endmembers to represent land cover types cannot fully account for the spectral variability of land cover types; moreover, these methods use these limited representations to calculate the membership function (MF), leading to limited classification and mapping performance. To address spectral variability and mixed pixels, this study proposes a novel MF generation method, in which mixed pixels are treated as an auxiliary type, and the clustering and spectral characteristics are integrated to detect the cores of land cover types in spectral space. Then, the spectral diversity can be fully expressed by the core components of the land cover types. The membership values of mixed pixels are calculated from the core components via the sparse reconstruction method. The experiment shows that the proposed method has a substantial increase in classification accuracy over existing methods.

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“…Finally, its effectiveness was verified on simulated data and real datasets. Bian et al proposed an adaptive fuzzy KNN algorithm [46]. Specifically, in the training process, it learns the optimal K value of each test data after reconstruction through sparse learning.…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

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Fuzzy KNN Method With Adaptive Nearest Neighbors

Cited by 28 publications

References 53 publications

Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

Generating Fuzzy Membership Functions for Modeling Wetland Ecosystems From Multispectral Remote Sensing Images

KNN Classification with One-step Computation

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