Incremental gradient improving decision-making tree updating method

Abstract

The invention discloses an incremental gradient improving decision-making tree updating method. The incremental gradient improving decision-making tree updating method comprises the following steps: A, sequencing and merging an incremental data set and an original data set to acquire the current newest sequential data set; B, calculating the newest optimal splitting attribute and splitting value of each node on the current newest sequential data set separately; and C, updating data classification models by utilizing the comparison result of the newest optimal splitting attribute and the optimal splitting attribute of the node before a new data block arrives. By the incremental gradient improving decision-making tree updating method, the existing data classification models can be quickly updated by an incremental method before new data sets arrive in batches, a new gradient improving decision-making tree classification model does not need to be trained again, and updating is conducted on the basis of the existing models, so that the time of establishing the data classification models is greatly reduced, the training speed of the models is increased and a large amount of time cost is saved.

Description

technical field [0001] The invention relates to an updating method of a data classification model, in particular to an incremental gradient-boosting decision tree updating method. Background technique [0002] In recent years, people have gradually realized the importance of data analysis, and began to analyze and mine data to discover the potential value of data. More and more fields such as finance, e-commerce, medical care and education have begun to use data mining technology to obtain the potential value of data. [0003] Among these applications and services, the more common requirement is data classification. In the data classification problem, how to quickly update the existing data classification model is an important research topic, and it is also an important challenge faced by many applications that need to process data in real time. Because, after the arrival of new data, it will take a lot of time to rebuild a new data classification model. [0004] Gradient...

AI Technical Summary

Problems solved by technology

However, when new data arrives, the existing gradient boosting decision tree needs to retrain a data classification model on all data sets, and does not make full use of the data classification model established on the original data set. For this, we designed An incremental gradient boosting decision tree method that quickly updates the established data classification model on the original data set when the new data set arrives

Images