Multi-target feature selection algorithm based on adaptive graph learning

被引：0

作者：

He D.-B. ^{[1
]}

Sun S.-X. ^{[1
]}

Liang X. ^{[1
]}

Xie L. ^{[1
]}

Zhang K. ^{[1
]}

机构：

[1] Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan

来源：

Kongzhi yu Juece/Control and Decision | 2024年 / 39卷 / 07期

关键词：

adaptive graph learning; alternating optimization algorithm; feature selection; multi-target regression; sparse regression;

D O I：

10.13195/j.kzyjc.2023.0127

中图分类号：

学科分类号：

摘要：

Feature selection not only enhances the efficiency of regression modelling but also reduces the detrimental effects of feature redundancy and noises. This paper proposes a multi-target feature selection algorithm based on adaptive graph learning. Specifically, the method imposes a low-rank constraint on the regression matrix, enabling simultaneous modelling of inter-target, input-output and inter-sample relationships within a general framework. The similarity-induced graph matrix is learned to adaptively preserve samples’ similarity structure to alleviate the influence of noises and outliers. Furthermore, we introduce a manifold regularizer to preserve the global target correlations to ensure the global target correlations structure of data in the subsequent learning process. An alternative optimization algorithm is presented to solve the final objective function. Extensive experiments conducted on real-world data sets demonstrate that the proposed method is superior to state-of-the-art multi-target feature selection methods. © 2024 Northeast University. All rights reserved.

引用

页码：2295 / 2304

页数：9

共 33 条

[1]

Borchani H, Varando G, Bielza C, Et al., A survey on multi-output regression, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5, 5, pp. 216-233, (2015)

[2]

Li H Q, Zhang W, Chen Y, Et al., A novel multi-target regression framework for time-series prediction of drug efficacy, Scientific Reports, 7, 1, pp. 1-9, (2017)

[3]

Kocev D, Dzeroski S, White M D, Et al., Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition, Ecological Modelling, 220, 8, pp. 1159-1168, (2009)

[4]

Musicki D., Multi-target tracking using multiple passive bearings-only asynchronous sensors, IEEE Transactions on Aerospace and Electronic Systems, 44, 3, pp. 1151-1160, (2008)

[5]

Zhen X T, Yu M Y, Islam A, Et al., Descriptor learning via supervised manifold regularization for multioutput regression, IEEE Transactions on Neural Networks and Learning Systems, 28, 9, pp. 2035-2047, (2017)

[6]

Wang X Q, Zhen X T, Li Q Z, Et al., Cognitive assessment prediction in alzheimer’s disease by multi-layer multi-target regression, Neuroinformatics, 16, 3, pp. 285-294, (2018)

[7]

Ghosn J, Bengio Y., Multi-task learning for stock selection, Proceedings of the 9th International Conference on Neural Information Processing Systems, pp. 946-952, (1996)

[8]

Chen B J, Chang M W, Lin C J., Load forecasting using support vector Machines: A study on EUNITE competition 2001, IEEE Transactions on Power Systems, 19, 4, pp. 1821-1830, (2004)

[9]

He X, Deng C, Niyogi P., Laplacian score for feature selection, Proceedings of the 18th International Conference on Neural Information Processing Systems, pp. 507-514, (2005)

[10]

Gu Q, Li Z, Han J., Generalized fisher score for feature selection, Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pp. 266-273, (2011)

← 1 2 3 4 →