Incremental feature selection by sample selection and feature-based accelerator

Yanyan Yang

doi:10.1016/j.asoc.2022.108800

2022-05-01

PRE

DOI：10.1016/j.asoc.2022.108800

原文链接

摘要

En 中文

Incremental feature selection is an efficient paradigm that updates an optimal feature subset from added-in data without forgetting the previously learned knowledge. Most existing studies of rough set based incremental feature selection require scanning all added-in samples and all possible candidate features when determining a best feature. However, such a classical search strategy has to perform some redundant calculations, which increase the computing and memory space resources. To avoid the redundant calculations, we propose a novel incremental feature selection method using sample selection and feature-based accelerator. First, a feature selection framework based on discernibility score is proposed as basis for our incremental method. Second, sample selection scheme is proposed to eliminate useless samples from added-in data. This scheme ensures that only useful samples are considered in the incremental process. Third, feature-based accelerator is designed to incrementally select a best feature and simultaneously remove redundant candidate features. It is theoretically guaranteed redundant features removed earlier remain redundant and will not be reexamined during the rest of the process. Finally, our incremental feature selection algorithm is designed by a two stage procedure including sample selection scheme and feature-based accelerator. The results of experiments validate the time efficiency of the proposed incremental algorithm, especially on datasets with numerous instances or high dimensions. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

Keyword:

Incremental feature selection

Feature selection

Rough set

Sample selection

Feature-based accelerator

期刊

Applied Soft Computing

IF:

6.6

论文数:

1.4W

被引数:

4.8W

机构

Beijing Jiaotong University

学者数:

2.2W

论文数: 1.7W

被引数: 1.2W

north china electric power university

学者数:

2.5W

论文数: 1.7W

被引数: 16

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