公开(公告)号：US20190042982A1

公开(公告)日：2019-02-07

申请号：US16132264

申请日：2018-09-14

Applicant: Alibaba Group Holding Limited

Inventor： Shenquan Qu ,  Jun Zhou ,  Yongming Ding

IPC: G06N99/00 ,  G06F17/30

Abstract: An automatic multi-threshold feature filtering method and an apparatus thereof are provided. In an iterative process of training a machine learning model, the feature filtering method calculates a feature filtering threshold and feature correlation values of a current round of iteration based on a result of a previous iteration, and performs feature filtering on samples based on the calculated feature filtering threshold and the calculated feature correlation values. The feature filtering apparatus of the present disclosure includes a calculation module and a feature filtering module. The method and apparatus of the present disclosure can automatically generate different feature filtering thresholds at each iteration, which greatly improves an accuracy of a filtering threshold, and can greatly increase the training speed of automatic machine learning and an accuracy of a machine learning model compared with fixed and single thresholds nowadays.

公开(公告)号：US11544618B2

公开(公告)日：2023-01-03

申请号：US16132264

申请日：2018-09-14

Applicant: Alibaba Group Holding Limited

Inventor： Shenquan Qu ,  Jun Zhou ,  Yongming Ding

IPC: G06F16/00 ,  G06N20/00 ,  G06K9/62 ,  G06F16/9535

Abstract: An automatic multi-threshold feature filtering method and an apparatus thereof are provided. In an iterative process of training a machine learning model, the feature filtering method calculates a feature filtering threshold and feature correlation values of a current round of iteration based on a result of a previous iteration, and performs feature filtering on samples based on the calculated feature filtering threshold and the calculated feature correlation values. The feature filtering apparatus of the present disclosure includes a calculation module and a feature filtering module. The method and apparatus of the present disclosure can automatically generate different feature filtering thresholds at each iteration, which greatly improves an accuracy of a filtering threshold, and can greatly increase the training speed of automatic machine learning and an accuracy of a machine learning model compared with fixed and single thresholds nowadays.