Ant-Based Feature and Instance Selection for Multiclass Imbalanced Data

被引:0
作者
Villuendas-Rey, Yenny [1 ]
Yanez-Marquez, Cornelio [2 ]
Camacho-Nieto, Oscar [1 ]
机构
[1] Inst Politecn Nacl, Ctr Innovac & Desarrollo Tecnol Computo, Mexico City 07700, Mexico
[2] Inst Politecn Nacl, Ctr Invest Comp, Mexico City 07738, Mexico
关键词
Feature extraction; Rough sets; Classification algorithms; Training; Metadata; Metaheuristics; Information systems; Nearest neighbor methods; Ant colony optimization; Algorithm design and theory; Multiclass imbalanced data; feature selection; instance selection; nearest neighbor; EVOLUTIONARY INSTANCE; ALGORITHMS; INFORMATION; SOFTWARE; SETS;
D O I
10.1109/ACCESS.2024.3418669
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper introduces a novel algorithm called Ant-based Feature and Instance Selection. This new algorithm addresses the simultaneous selection of instances and features for mixed, incomplete, and imbalanced data in the context of lazy instance-based classifiers. The proposed algorithm uses a hybrid selection strategy based on metaheuristic procedures and Rough Sets. The Ant-based Feature and Instance Selection algorithm combines Ant Colony Optimization and Generic Extended Rough Sets for Mixed and Incomplete Information Systems. It has five stages: reduct computation, metadata computation, intelligent instance preprocessing, submatrices creation, and fusion. To test the performance of the proposed algorithm, we used 25 datasets from the Machine Learning repository of the University of California at Irvine. All these datasets are imbalanced, with multiple classes and represent real-world classification problems. The number of classes ranges between three and eight classes. Most of them also have mixed or incomplete descriptions. We used several performance measures and computed the Instance Retention ratio and the Feature Retention ratio. To determine the existence or not of significant differences in the performance of the compared algorithms, we used non-parametric hypothesis testing. The statistical analysis results confirm the high quality of the proposed algorithm for selecting features and instances in multiclass imbalanced data.
引用
收藏
页码:133952 / 133968
页数:17
相关论文
共 66 条
[11]   SMOTE: Synthetic minority over-sampling technique [J].
Chawla, Nitesh V. ;
Bowyer, Kevin W. ;
Hall, Lawrence O. ;
Kegelmeyer, W. Philip .
2002, American Association for Artificial Intelligence (16)
[12]   Feature selection for imbalanced data based on neighborhood rough sets [J].
Chen, Hongmei ;
Li, Tianrui ;
Fan, Xin ;
Luo, Chuan .
INFORMATION SCIENCES, 2019, 483 :1-20
[13]   NEAREST NEIGHBOR PATTERN CLASSIFICATION [J].
COVER, TM ;
HART, PE .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1967, 13 (01) :21-+
[14]   A Comparative Survey of Instance Selection Methods applied to Non-Neural and Transformer-Based Text Classification [J].
Cunha, Washington ;
Viegas, Felipe ;
Franca, Celso ;
Rosa, Thierson ;
Rocha, Leonardo ;
Goncalves, Marcos Andre .
ACM COMPUTING SURVEYS, 2023, 55 (13S)
[15]   Multi-granularity relabeled under-sampling algorithm for imbalanced data [J].
Dai, Qi ;
Liu, Jian-wei ;
Liu, Yang .
APPLIED SOFT COMPUTING, 2022, 124
[16]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[17]   Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection [J].
Derrac, Joaquin ;
Cornelis, Chris ;
Garcia, Salvador ;
Herrera, Francisco .
INFORMATION SCIENCES, 2012, 186 (01) :73-92
[18]   IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule [J].
Derrac, Joaquin ;
Garcia, Salvador ;
Herrera, Francisco .
PATTERN RECOGNITION, 2010, 43 (06) :2082-2105
[19]   Ant colony optimization theory: A survey [J].
Dorigo, M ;
Blum, C .
THEORETICAL COMPUTER SCIENCE, 2005, 344 (2-3) :243-278
[20]  
Fernández A, 2011, LECT NOTES ARTIF INT, V6678, P1, DOI 10.1007/978-3-642-21219-2_1