关键词: 大数据预测问题, 参数自适应, 支持向量回归, 均值漂移聚类, 海洋捕食者算法

Abstract: To solve the efficient and accurate prediction issues for big data,a parameter adaptive Support Vector Regression (SVR) was proposed based on the Mean Shift Clustering (MSC) and Marine Predators Algorithm (MPA).The large sample was divided into training data,validation data and test data.The MSC algorithm was used to deal with the training data for the Cluster Centers (CCs).The SVR parameters were set as kernel function type,penalty factor and kernel function coefficient,and multiple SVR parameter sets were generated randomly.Based on SVR parameter sets and CCs,the SVR was applied to predict the validation data for obtaining the prediction accuracy.The MPA was used to update the SVR parameter sets,and this step was repeated to obtain the optimal SVR parameter set until the cut-off condition was met.The prediction accuracy of the test data was achieved with the SVR and the optimal parameter set.Compared with the highly similar algorithm in prediction accuracy,stability and data loss,the feasibility and effectiveness of the approach were verified.

Key words: big data prediction issue, parameter self-adaption, support vector regression, mean shift clustering, marine predators algorithm