Application of KNN algorithm incorporating Gaussian functions in green and high-quality development of cities empowered by circular economyCIRCULAR economyECONOMIC forecastingTIME series analysisSUPPORT vector machinesAUTOREGRESSIVE modelsA growing number of industries have started to adapt to the circular ...
Therefore, all the predictive values in the training phase are perfectly fitted to the actual values, which are determined by the characteristics of the KNN algorithm. Overall, all models demonstrate satisfactory performance in the training phase, and developing predictive models based on small-sample...
hyper plane is greater than the given threshold,SVM algorithm is applied to classify test samples,otherwise KNN algorithm would be used to reduce the misclassification probability.Results of experiments show this method of SVM-KNN algorithm has gained the accuracy of 95.86% on abnormal behavior ...
After fusing the extracted features, machine learning algorithms such as k-nearest neighbors (KNN) and SVM are used for classification. The results showed that the SAE-CNN-SVM model had the best recognition performance for mold level detection in corn seeds. Zhang et al. (2021) graded corn ...
[24] proposed a new KNN algorithm that improved the pruning process of the LC-KNN. The results showed their method performed better than recent related works. Simon et al. [25] evaluated the performance of logistic regression and other ML algorithms to predict the risk of cardiovascular ...
Such algorithms of text categorization as Naiuml;ve Bayes, kNN, Decision Tree and Boosting can be applied in spam filtering. However, the effectiveness of Naiuml;ve Bayes is limited and it is not fit for instant feedback learning. Others algorithm such as SVM are more effective but ...
All these algorithms could do good on whether there is hydrate in the sediment, of which KNN algorithm was shown the best. Therefore, machine-learning-based methods could improve the identification accuracy of gas hydrate.天然气水合物是一种似冰状固体化合物,由水分子和气体分子在低温高压条件下生成...
This study based on multisource data which included optical and LiDAR data and used random forest feature selection algorithm to build optimal KNN model for the estimation of FAGB estimation. The result showed the RMSE and R2 of the optimal KNN model are 20.12 ton/hm2 and 0.8 respectively ...
The KNN algorithm is a nonparametric learning technique that may be applied to both classification and regression applications [29]. KNN classifies or predicts the grouping of individual data points based on distances between them. Typically, a lower K number should result in improved accuracy because...
To solve bankruptcy prediction tasks, we proposed an improved rime optimization technique (RMRIME). The proposed RMRIME algorithm first employs roulette wheel selection step, introducing random individuals into the position updating process to expand the search space and boost the RMRIME’s exploration...