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.

Date of Patent:
Oct. 07, 2025

Filed:

Dec. 31, 2021
Applicant:

Sun Yat-sen University, Guangzhou, CN;

Inventors:

Xingcheng Liu, Guangzhou, CN;

Yingying Zhao, Guangzhou, CN;

Yitong Liu, Guangzhou, CN;

Assignee:

Sun Yat-sen University, Guangzhou, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/245 (2023.01); G06F 18/2132 (2023.01); G06F 18/2411 (2023.01); G06F 18/243 (2023.01); H04W 64/00 (2009.01);
U.S. Cl.
CPC ...
G06F 18/245 (2023.01); G06F 18/21322 (2023.01); G06F 18/2411 (2023.01); G06F 18/24323 (2023.01); H04W 64/006 (2013.01);
Abstract

A method of hop count matrix recovery based on a decision tree classifier, includes: S: performing a flooding process to acquire a hop count matrix {tilde over (H)} with missing entries; S: constructing a training sample set according to relationships between a part of observed hop counts in the hop count matrix {tilde over (H)}, and modeling the observed hop counts in the hop count matrix as labels of the training sample set, wherein a maximum hop count represents a number of classes; S: training a decision tree classifier according to the training sample set obtained in step S; and S: constructing a feature for an unobserved hop count, to obtain an unknown sample; and inputting the unknown sample to the trained decision tree classifier, to obtain a class of the unknown sample which represents a missing hop count at a corresponding position in the matrix, to recover a complete hop count matrix H.


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