Efficient feature selection for logical analysis of large-scale multi-class datasets

被引：3

作者：

Yan, Kedong ^{[1
]}

Miao, Dongjing ^{[2
]}

Guo, Cui ^{[3
]}

Huang, Chanying ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, 200 Xiaolingwei, Nanjing 210094, Peoples R China

[2] Harbin Inst Technol, Fac Comp, 92 Xidazhi, Harbin 150001, Peoples R China

[3] Shantou Univ, Business Sch, 243 Daxue Rd, Shantou 515063, Peoples R China

来源：

JOURNAL OF COMBINATORIAL OPTIMIZATION | 2021年 / 42卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Logical Analysis of Data; Supervised Learning; Feature Selection; Multi-classification; Set Covering;

D O I：

10.1007/s10878-021-00732-2

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Feature selection in logical analysis of data (LAD) can be cast into a set covering problem. In this paper, extending the results on feature selection for binary classification using LAD, we present a mathematical model that selects a minimum set of necessary features for multi-class datasets and develop a heuristic algorithm that is both memory and time efficient for this model correspondingly. The utility of the algorithm is illustrated on a small example and the superiority of our work is demonstrated through experiments on 6 real-life multi-class datasets from UCI repository.

引用

页码：1 / 23

页数：23

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