Improved Decision Tree Algorithm for Big Data Classification Optimization
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.
decision tree modelgrey wolf optimization algorithmobjective functionbig data classificationfuzzy decision function
万方数据
维普
