Performance Optimization Model of Molecular Dynamics Simulation Based on Machine Learning and Data Mining Algorithm

Table 1

KNN optimization direction and method.

Direction of optimization	Optimization method	Specific operation
Improve classification accuracy	Weighted based on attribute characteristics	Assign different weights to different attributes according to their impact in the classification
	Weighted based on distance features	Assign different weights to different nearest neighbor samples according to the distance
Improve classification efficiency	Feature attribute dimension reduction	Dimension reduction, feature selection, and extraction are carried out on the sample data set
	Speed up algorithm search	For example, the big data sample is divided into blocks, and then the block closest to the sample to be tested is found during the test and used as a new training sample for K-nearest neighbor classification