Zhang Chenrui, a graduate majoring in Math in the class of 2020 published the article, Semi-supervised Feature Selection with Soft Label Learning, as the first author on IEEE/CAA Journal of Automatica Sinica. Yu Bo, who is a professor in the School of Mathematics Science devoted himself to direct Chengrui to complete this research.

As the lead author, Chengrui spent nearly two years to finish this article. For achieving the goal, he tried his best to build the model and deduce it mathematically, conduct the experiments and work on the thesis. Besides, he participated in and voluntarily served as the team leader in scientific research competitions and won the M Prize of Mathematical Contest In Modeling(MCM), the First Prize of Asia-Pacific Mathematical Contest In Modeling(APMCM) and Special Award of the 10th Annual Conference of Innovation and entrepreneurship for College Students of Liaoning Province.

Fig.1 Frame diagram of SFS-SLL

Abstract

With the rapid increase of high-dimensional data mixed with labelled and unlabelled samples, the semi-supervised feature selection technique has received much attention in recent years. However, most existing approaches ignore the fuzziness of the data. Moreover, many feature selection methods need to measure the relationships among all samples, which is inefficient and difficult to be applied to large-scale data. To address the problems mentioned above, we propose an effective semi-supervised feature selection with the soft label learning (SFS-SLL) method in this paper. Specifically, we first learn initial soft labels based on the local distance between samples and clustering centers using an efficient fuzzy C-means clustering. We propose a supervised semantic constraint to exploit labelled and unlabelled data using manual labels as the soft label learning guidance. Then, we propose a simple yet effective sparse regression model which integrates soft label learning and feature selection into a unified framework. Finally, we derive an effective optimization strategy based on the alternating direction method of multipliers (ADMM) to iteratively solve the formulated problem. Experiment results on several benchmark datasets show a performance improvement on feature selection accuracy and efficiency over compared methods.

Journal Metrics

▪ JCR Impact Factor: 7.847

Rank：Top 10% (7/65), Category of Automation & Control Systems

Quantile: The 1st (SCI Q1)

▪ CiteScore : 13.0

Rank: Top 1 (Control and Optimization), Top 5% (Information System) , Top 5% (Control and Systems Engineering), Top 7% ( Artificial Intelligence)

Quantile: The 1st (Q1)

▪ Google Scholar h5-index: 64  

Top 7 (Automation & Control)

Source: Semi-supervised Feature Selection with Soft Label Learning

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