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...
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 ...
当KNN算法用于推荐系统时,可以对用户的搜索模式进行分类,并预测用户的未来偏好。通过分析用户行为数据的模式,如用户的Web服务器日志和点击流数据,可以用来对与用户口味相似的项目进行分类,然后用结果推荐合适的项目。另一方面,Jannach等人[68]发表了一项研究结果,表明使用KNN算法研究的推荐模型的性能较差。由于KNN必须选择...
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 ...
[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 ...
The dataset contained a total of 10,641 children. They applied three popular ML-based algorithms, namely RF, LR, and KNN for the prediction of mortality risk among children. Based on the systematic assessment of the performance parameters, they proposed that RF-based algorithm was superlative. ...
KNN works by looking at the ‘k’ nearest data points to the point in question in the dataset, where ‘k’ can be any integer. Based on the values of these nearest neighbors, the algorithm then makes its prediction. For example, the prediction might be the most common class among the ...
Although each of these approaches uses different methods in their experimentation, a systematic literature review (SLR) shows that the application of each algorithm mirrors performance metrics to determine the accuracy with which it predicts that a financial transaction is fraud. Such metrics include ...
sizes, but it is more likely to cause misclassification for smaller sample sizes, while the SVM can solve the problem in small sample cases. The test results show that SVM is better than KNN algorithm, which proves that SVM has strong generalization ability for classification of insufficient ...