Research progress of pattern classification based on machine learning

doi:10.11996/JG.j.2095-302X.2023030415

Abstract

Abstract:

This paper presented a review of the research on pattern classification combined with machine learning at home and abroad. The study systematically sorted out the research methods, literature data analysis, pattern classification and machine learning applications, aiming to understand the current research status and research progress at home and abroad. This paper also summarized the methods and shortcomings of pattern classification and looked forward to the future development, providing reference for further exploration. Based on a large number of literature studies, CiteSpace software was employed to analyze the current research hotspots and trends. The methods, classical models, and the application of machine learning in pattern classification used in dataset construction, data processing, feature extraction, and pattern classification were analyzed and summarized in detail. The pattern classification research had a tendency of evolving from traditional manual classification to machine classification. The accurate and efficient acquisition of target pattern features can effectively improve the classification effect. There is a lack of databases in pattern classification research, which hinders the in-depth study of pattern classification. The systematic pattern database can be constructed by intelligent classification methods. The classification technology can be applied to the service system platform to realize the living inheritance of patterns. On the basis of classification, combined with various combination algorithms, innovative design of patterns can be carried out to promote the further development of patterns.

Key words: CiteSpace, visual analysis, research hotspots and trends, pattern classification, machine learning

CLC Number:

TB472

BIAN Kun, LIANG Hui. Research progress of pattern classification based on machine learning[J]. Journal of Graphics, 2023, 44(3): 415-426.

Figures/Tables 12

References 46

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序号	出现频次	关键词	中介中心性	出现年
1	64	深度学习	0.40	2007
2	42	迁移学习	0.12	1990
3	14	特征提取	0.19	2008
4	6	特征融合	0.11	2008
5	9	神经网络	0.09	2007

序号	出现频次	关键词	中介中心性	出现年
1	64	深度学习	0.40	2007
2	42	迁移学习	0.12	1990
3	14	特征提取	0.19	2008
4	6	特征融合	0.11	2008
5	9	神经网络	0.09	2007

序号	出现频次	关键词	中介中心性	出现年
1	130	deep learning	0.27	2015
2	81	neuralnetwork	0.14	2017
3	77	classification	0.39	2015
4	42	feature extraction	0.24	2018
5	41	neural network	0.15	2017

序号	出现频次	关键词	中介中心性	出现年
1	130	deep learning	0.27	2015
2	81	neuralnetwork	0.14	2017
3	77	classification	0.39	2015
4	42	feature extraction	0.24	2018
5	41	neural network	0.15	2017

经典网络模型	时间(年)	优势	不足
AlexNet	2012	以ReLu激活函数替代Sigmoid函数，避免梯度消失，加快收敛速度， Dropout降低过拟合，泛化能力增强	对所需训练的数据量需求较大，易造成计算量大的问题、结构层深度增加易引起过拟合、梯度消失或爆炸
VGGNet	2014	使用小卷积核代替大卷积核，网络模型结构更深、更宽，表现效果更好	相比AlexNet需要更大的数据量，训练耗费时间较长、深度增加易引起过拟合、梯度消失或爆炸
GoogleNet	2015	Inception结构使模型加深、加宽的同时稀疏连接可减少参数以及计算量、密集矩阵实现高计算性能	网络深度增加不利于模型的收敛
ResNet	2016	残差连接使模型结构更深、性能更强更高效；通道数增加利于高级特征的获取	特征过度重用，特征冗余
DenseNet	2017	拓展网络连接、信息流最大化、增强信息传播、参数量减少、支持特征重用	训练耗费时间较长，在小样本情况下容易过拟合