A hybrid machine learning framework by incorporating categorical boosting and manifold learning for financial analysis DOI Creative Commons

Yuyang Zhao,

Hongbo Zhao

Intelligent Systems with Applications, Год журнала: 2024, Номер unknown, С. 200473 - 200473

Опубликована: Дек. 1, 2024

Язык: Английский

Multi-view clustering via consensus coefficient matrix and separate segmentation matrices DOI Creative Commons
Fatemeh Sadjadi, Mina Jamshidi, Amir Asadi

и другие.

Journal of Information and Telecommunication, Год журнала: 2025, Номер unknown, С. 1 - 18

Опубликована: Март 25, 2025

Процитировано

0
I2QD: Unsupervised feature selection via information quality, quantity, and difference degree DOI
Pengfei Zhang, Yuxin Zhao, Lvhui Hu

и другие.

Information Processing & Management, Год журнала: 2025, Номер 62(5), С. 104173 - 104173

Опубликована: Апрель 9, 2025

Язык: Английский

Процитировано

0
RFAE: A high-robust feature selector based on fractal autoencoder DOI
Jingfeng Ou, Jiawei Li, Zhiliang Xia

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127519 - 127519

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
BCDAN: A balanced method for community detection in attributed networks DOI
Yuchen Liu,

Yabin Peng,

Hongchang Chen

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 130411 - 130411

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Star: semi-supervised tripartite attribute reduction DOI
Keyu Liu,

Damo Qian,

Tianrui Li

и другие.

International Journal of Machine Learning and Cybernetics, Год журнала: 2024, Номер unknown

Опубликована: Дек. 7, 2024

Язык: Английский

Процитировано

0
Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis DOI Creative Commons

Fernanda Aguiar,

Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

и другие.

Animals, Год журнала: 2024, Номер 14(24), С. 3626 - 3626

Опубликована: Дек. 16, 2024

A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking dynamic complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency segmentation fidelity object-based machine learning applications. The integrates convolutional operations, quantization strategies, polynomial transformations optimize visual environments, addressing limitations of traditional pixel-level unsupervised methods. innovative approach enhances delineation generates structured metadata, facilitating robust feature extraction consistent representation across varied conditions. As empirical validation shows, proposed pipeline reduces demands while improving accuracy, particularly intricate backgrounds. Key features include adaptive segmentation, efficient metadata creation, scalability real-time methodology’s application domains such as Precision Livestock Farming autonomous systems highlights its potential high-accuracy data processing. Future work will explore parameter optimization adaptability diverse datasets further refine capabilities. presents scalable framework designed advance applications tasks by incorporating methodologies automated segmentation.

Язык: Английский

Процитировано

0
A hybrid machine learning framework by incorporating categorical boosting and manifold learning for financial analysis DOI Creative Commons

Yuyang Zhao,

Hongbo Zhao

Intelligent Systems with Applications, Год журнала: 2024, Номер unknown, С. 200473 - 200473

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0