Toward an Optimal and Structured Feature Subset Selection for Multi-Target Regression Using Genetic Algorithm

被引:0
|
作者
Syed, Farrukh Hasan [1 ]
Tahir, Muhammad Atif [1 ]
Frnda, Jaroslav [2 ,3 ]
Rafi, Muhammad [1 ]
Anwar, Muhammad Shahid [4 ]
Nedoma, Jan [3 ]
机构
[1] Natl Univ Comp & Emerging Sci, Sch Comp, Dept Comp Sci, Islamabad 44000, Pakistan
[2] Univ Zilina, Fac Operat & Econ Transport & Commun, Dept Quantitat Methods & Econ Informat, Zlina 01026, Slovakia
[3] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Dept Telecommun, Ostrava 70800, Czech Republic
[4] Gachon Univ, Dept AI & Software, Seongnam Si 13120, South Korea
关键词
Multi-target regression; feature selection; genetic algorithm; single target; multiple objectives; PREDICTION; ENSEMBLES; QUALITY; MODEL;
D O I
10.1109/ACCESS.2023.3327870
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multi Target Regression (MTR) is a machine learning method that simultaneously predicts multiple real-valued outputs using a set of input variables. A lot of emerging applications that can be mapped to this class of problem. In MTR method one of the critical aspect is to handle structural information like instance and target correlation. MTR algorithms attempt to exploit these interdependences when building a model. This results in increased model complexities, which in turn, reduce the interpretability of the model through manual analysis of the result. However, data driven real-world applications often require models that can be used to analyze and improve real-world workflows. Leveraging dimensionality reduction techniques can reduce model complexity while retaining the performance and boost interpretability. This research proposes multiple feature subset alternatives for MTR using genetic algorithm, and provides a comparison of the different feature subset selection alternatives in conjunction with MTR algorithms. We proposed a genetic algorithm based feature subset selection with all targets and with individual target keeping the structural information intact in the selection process. Experiments are performed on real world benchmarked MTR data sets and the results indicate that a significant improvement in performance can be obtained with comparatively simple MTR models by utilizing optimal and structured feature selection.
引用
收藏
页码:121966 / 121977
页数:12
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