2022
DOI: 10.48550/arxiv.2201.04343
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

An Efficient and Adaptive Granular-ball Generation Method in Classification Problem

Abstract: Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granularball generation using the division to replace k-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granul… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 26 publications
0
1
0
Order By: Relevance
“…However, the above emotion detection models overlook the effects of each word on emotion in dialogue. Inspired by the idea of multi-granularity computing [20][21][22], we note that in an actual multi-round conversation, people can express their emotions through sentiment and non-sentiment words. Therefore, it is crucial for comprehending emotions to perceive the emotions of fine-grained sentences, i.e., words.…”
Section: Introductionmentioning
confidence: 99%
“…However, the above emotion detection models overlook the effects of each word on emotion in dialogue. Inspired by the idea of multi-granularity computing [20][21][22], we note that in an actual multi-round conversation, people can express their emotions through sentiment and non-sentiment words. Therefore, it is crucial for comprehending emotions to perceive the emotions of fine-grained sentences, i.e., words.…”
Section: Introductionmentioning
confidence: 99%