Data mining and social networks processing method based on support vector machine and k-nearest neighbor

Lu, Youli; Li, Jintong; Yang, Zhiyuan; Ou, Xianfeng; Xie, Wenwu

doi:10.3233/jcm-204613

Cited by 4 publications

(2 citation statements)

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“…We use "r 1 8 � 1" to denote the accuracy of the proposed BLA-KNN method. We use "r 1 7 � 2" to denote the accuracy ranking of LLK method. Please note, the last in Table 5 lists the average ranking of each adopted method for all the adopted datasets.…”

Section: Evaluation Criterionmentioning

confidence: 99%

“…Te classifcation problem is an important branch of machine learning [1][2][3][4], and because the training process of k nearest neighbor (i.e., KNN) classifcation method does not require the assumption of consistent data distribution, this advantage makes it an irreplaceable method for all classifcation algorithms. In addition, the KNN classifcation method has other characteristics, for example, it is nonparameter learning, i.e., no training parameters, and the Euclidean distance between the training samples and testing sample are used to predict the label of the testing sample.…”

Section: Introductionmentioning

confidence: 99%

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Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Zhang,

Bian,

Wang

2024

International Journal of Intelligent Systems

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While the classical KNN (k nearest neighbor) shares its avoidance of the consistent distribution assumption between training and testing samples to achieve fast prediction, it still faces two challenges: (a) its generalization ability heavily depends on an appropriate number k of nearest neighbors; (b) its prediction behavior lacks interpretability. In order to address the two challenges, a novel Bayes-decisive linear KNN with adaptive nearest neighbors (i.e., BLA-KNN) is proposed to obtain the following three merits: (a) a diagonal matrix is introduced to adaptively select the nearest neighbors and simultaneously improve the generalization capability of the proposed BLA-KNN method; (b) the proposed BLA-KNN method owns the group effect, which inherits and extends the group property of the sum of squares for total deviations by reflecting the training sample class-aware information in the group effect regularization term; (c) the prediction behavior of the proposed BLA-KNN method can be interpreted from the Bayes-decision-rule perspective. In order to do so, we first use a diagonal matrix to weigh each training sample so as to obtain the importance of the sample, while constraining the importance weights to ensure that the adaptive k value is carried out efficiently. Second, we introduce a class-aware information regularization term in the objective function to obtain the nearest neighbor group effect of the samples. Finally, we introduce linear expression weights related to the distance measure between the testing and training samples in the regularization term to ensure that the interpretation of Bayes-decision-rule can be performed smoothly. We also optimize the proposed objective function using an alternating optimization strategy. We experimentally demonstrate the effectiveness of the proposed BLA-KNN method by comparing it with 7 comparative methods on 15 benchmark datasets.

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Section: Evaluation Criterionmentioning

confidence: 99%