Due to the complex structure and features of current massive data,big data exhibits unstructured and small sample characteristics,making it difficultto ensure high accuracy and efficiency in its classification.Therefore,a big data classification optimization method is proposed to improve the decision tree algorithm.A fuzzy decision function is constructed to detect sequence features of big data,and these features inputted into a decision tree model to mine and train rules.The decision tree model is improved using grey wolf optimization algorithm.The big data is classified using the improved model,and then a classifier accuracy objective function is established to achieve accurate classification of big data.The experimental results show that the proposed method achieves the highest accuracy in classification results and the lowest false positive case rate,ensuring the overall high throughput of the algorithm and improving its classification efficiencv.
关键词
决策树模型/灰狼优化算法/目标函数/大数据分类/模糊决策函数
Key words
decision tree model/grey wolf optimization algorithm/objective function/big data classification/fuzzy decision function
维普
万方数据