Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China; Institute of Data and Knowledge Engineering, School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.
Department of Computer Science and School of Data Science, City University of Hong Kong, Kowloon, Hong Kong; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong, China.
Neural Netw. 2021 Oct;142:180-191. doi: 10.1016/j.neunet.2021.04.038. Epub 2021 May 7.
Feature selection is a crucial step in data processing and machine learning. While many greedy and sequential feature selection approaches are available, a holistic neurodynamics approach to supervised feature selection is recently developed via fractional programming by minimizing feature redundancy and maximizing relevance simultaneously. In view that the gradient of the fractional objective function is also fractional, alternative problem formulations are desirable to obviate the fractional complexity. In this paper, the fractional programming problem formulation is equivalently reformulated as bilevel and bilinear programming problems without using any fractional function. Two two-timescale projection neural networks are adapted for solving the reformulated problems. Experimental results on six benchmark datasets are elaborated to demonstrate the global convergence and high classification performance of the proposed neurodynamic approaches in comparison with six mainstream feature selection approaches.
特征选择是数据处理和机器学习中的关键步骤。虽然有许多贪婪和顺序特征选择方法可用，但最近通过分数规划，通过同时最小化特征冗余和最大化相关性，开发了一种整体神经动力学监督特征选择方法。鉴于分数目标函数的梯度也是分数的，因此需要替代的问题公式来避免分数复杂性。在本文中，分数规划问题公式通过不使用任何分数函数，被等效地重新表述为双层和双线性规划问题。两个双时间尺度投影神经网络被用于求解所提出的重新表述的问题。在六个基准数据集上的实验结果详细说明了与六个主流特征选择方法相比，所提出的神经动力学方法在全局收敛性和高分类性能方面的优势。
