基于GeneticAlgorithm和弹性网的混合两层特征选择方法
A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net
摘要：
特征选择作为机器学习的关键预处理步骤，其目的是从高维特征空间数据集中确定具有代表性的预测因子，以提高预测精度。然而，增加的特征空间的维数，相比的观察的数量，提出了一个严峻的挑战，许多现有的特征选择方法的计算效率和预测性能。本文提出了一种新的混合两层特征选择方法，该方法结合了包装器和嵌入式方法来构造一个合适的预测子集。在第一层中，采用遗传算法（GA）作为一个包装器来搜索最优的预测子集，其目的是减少预测器的数量和预测误差。作为一种元启发式方法，遗传算法是选择由于其计算效率，但遗传算法并不保证最优性。为了解决这个问题，第二层被添加到所提出的方法，以消除任何剩余的冗余/不相关的预测，以提高预测精度。由于弹性网在调整正则化过程中惩罚项的灵活性和时间效率，被选为第二层的嵌入方法。这种混合两层方法已应用于NAM群体的玉米遗传数据集，该数据集由多个数据集子集组成，具有不同的预测因子数量与观测数量的比例。数值结果证实了所提出的模型的优越性。
英文摘要：
Feature selection, as a critical pre-processing step for machine learning, aims at determining representative predictors from a high-dimensional feature space dataset to improve the prediction accuracy. However, the increase in feature space dimensionality, comparing to the number of observations, poses a severe challenge to many existing feature selection methods with respect to computational efficiency and prediction performance. This paper presents a new hybrid two-layer feature selection approach that combines a wrapper and an embedded method in constructing an appropriate subset of predictors. In the first layer of the proposed method, the Genetic Algorithm(GA) has been adopted as a wrapper to search for the optimal subset of predictors, which aims to reduce the number of predictors and the prediction error. As one of the meta-heuristic approaches, GA is selected due to its computational efficiency; however, GAs do not guarantee the optimality. To address this issue, a second layer is added to the proposed method to eliminate any remaining redundant/irrelevant predictors to improve the prediction accuracy. Elastic Net(EN) has been selected as the embedded method in the second layer because of its flexibility in adjusting the penalty terms in regularization process and time efficiency. This hybrid two-layer approach has been applied on a Maize genetic dataset from NAM population, which consists of multiple subsets of datasets with different ratio of the number of predictors to the number of observations. The numerical results confirm the superiority of the proposed model.
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