The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
May. 02, 2023
Filed:
Sep. 01, 2020
Shenzhen Academy of Inspection and Quarantine, Shenzhen, CN;
Shenzhen Customs Information Center, Shenzhen, CN;
Shenzhen Customs Animal and Plant Inspection and Quarantine Technology Center, Shenzhen, CN;
Xianyu Bao, Shenzhen, CN;
Yina Cai, Shenzhen, CN;
Zhouxi Ruan, Shenzhen, CN;
Yun Guo, Shenzhen, CN;
Shaojing Wu, Shenzhen, CN;
Tikang Lu, Shenzhen, CN;
Zhinan Chen, Shenzhen, CN;
SHENZHEN ACADEMY OF INSPECTION AND QUARANTINE, Shenzhen, CN;
SHENZHEN CUSTOMS INFORMATION CENTER, Shenzhen, CN;
SHENZHEN CUSTOMS ANIMAL AND PLANT INSPECTION AND QUARANTINE TECHNOLOGY CENTER, Shenzhen, CN;
Abstract
The present disclosure provides an unbalanced sample classification method and an unbalanced sample classification apparatus. The method includes: obtaining unbalanced sample data; calculating a sample contribution rate based on the sample data and the characteristic data; filtering out a part of the sample data within a preset sample contribution threshold according to the sample contribution rate to determine as target sample data; and inputting the target sample data into a sample classification model to calculate a sample classification result through a classification algorithm. By using two variables of the characteristic value contribution rate and the characteristic contribution rate, the characteristics and samples with low contribution rate for classification are eliminated to effectively reducing the processing of unbalanced sample data, and a machine learning classification algorithm can be used on this basis to adopt the effective characteristics or samples to achieve efficient classification.