Open Access

ARTICLE

Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

Chuqing Zhang¹, Jiangyuan Yao^{2, *}, Guangwu Hu³, Thomas Schøtt⁴

1 School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
2 School of Computer Science & Cyberspace Security, Hainan University, Haikou, 570228, China.
3 School of Computer Science, Shenzhen Institute of Information Technology, Shenzhen, 518172, China.
4 The Faculty of Business and Social Science, University of Southern Denmark, Kolding, DK-6000, Denmark.

* Corresponding Author: Jiangyuan Yao. Email: .

Computers, Materials & Continua 2020, 64(3), 1741-1753. https://doi.org/10.32604/cmc.2020.010306

Received 25 February 2020; Accepted 18 April 2020; Issue published 30 June 2020

Abstract

Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones. The FGDKNN is based on a gradient decent learning algorithm to update weight. It updatesthe weight of labels by minimizing error ratio iteratively, so that the importance of attributes can be described better. We investigate the performance of FGDKNN with Beijing Innofund. The results show that FGDKNN performs about 23%, 20%, 18%, 15% better than KNN, SVM, DT and ANN, respectively. Moreover, the FGDKNN has fast convergence time under different training scales, and has good performance under different settings.

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Citations