A recessive active learning method: enhancing the performance of predict models by adjusting the structure of data space

Weiyu Sun; Yin Chen; Chunyu Ji

doi:10.1117/12.2624912

13 December 2021 A recessive active learning method: enhancing the performance of predict models by adjusting the structure of data space

Weiyu Sun, Yin Chen, Chunyu Ji

Author Affiliations +

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871W (2021) https://doi.org/10.1117/12.2624912
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

While training predict models to assess new data, relative researchers always incline to design some strategies to reduce amount of selected sampling points to save cost and time, especially when information about these sampling points is hard to get. Among these strategies, active learning is a popular and useful tool to enhance the efficiency of training predict model by improving the quality of sampling points selected. In biological field, making specific experiments to get result could cost some time and budgets, which fits the situation exactly. While when the information on each data points is limited, active learning method is relatively hard to apply. Thus, in order to relieve this situation, this paper attempts to solve the problem by optimizing the structure of datasets. In the paper, a specific dataset is selected to test the performance of several traditional active learning methods. Meanwhile, a special trick which aims to optimize the configuration of data space is proposed to enhance the performance of both predict models and active learning methods. According to experiments, it turns out that the optimization on data space could let the predict model fit better to the datasets and could help enhance the effect on active learning methods, which has performance enhancement of 5%~22% during the process (5%~20%) of training predict model. By combining with traditional active learning method, the increment could be risen up to 9%~32% under the same progress (5%, 10%, 15%, 20%), which stands for the percentage of data used to train predict model in all the dataset.

Citation Download Citation

Weiyu Sun, Yin Chen, and Chunyu Ji "A recessive active learning method: enhancing the performance of predict models by adjusting the structure of data space", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871W (13 December 2021); https://doi.org/10.1117/12.2624912

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Process modeling

Molecules

Performance modeling

Databases

Optimization (mathematics)

Machine learning

Keywords/Phrases

Search In:

Publication Years