@inproceedings{50afcc8d224b4fee85b9d2fa550082ca,
title = "Bio-invasion: A prediction model based on multi-objective optimization",
abstract = "In some real application scenarios, it is difficult to obtain enough labeled data and get an ideal classification result. Unsupervised learning and few-shot classification are widely used for this kind of tasks. In contrast to prior approaches, we propose a multi-objective optimization method, combining both location and image information. We establish models for these two kinds of information and calculate Pareto frontier in multi-objective programming, which enhances the interpretability with clear evaluation indicators. Our method is applied in the detection on Asian giant hornet and attains favorable results across few-shot datasets.",
keywords = "Gaussian Mixture Model, Pareto frontier, convolutional neural network, few-shot dataset, prediction",
author = "Ruijie Zhu and Caini Fan and Zidie Chen and Rugui Yao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021 ; Conference date: 17-08-2021 Through 19-08-2021",
year = "2021",
month = aug,
day = "17",
doi = "10.1109/ICSPCC52875.2021.9564706",
language = "英语",
series = "Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021",
}