基于差分进化的因子分解机算法

因子分解机(Factorization Machine,FM) 算法是一种基于矩阵分解的机器学习算法,可用于求解回归、分类和排序等问题。FM模型中的参数求解使用的是基于梯度的优化方法,然而在样本较少的情况下,该优化方法收敛速度慢,且易陷入局部最优。差分进化算法(Differential Evolution,DE)是一种启发式的全局优化算法,具有收敛速度快等特性。为提高FM模型的训练速度,利用DE计算FM模型参数,提出了DE-FM算法。在数据集Diabetes、HorseColic以及音乐分类数据集Music上的实验结果表明,改进后的基于差分进化的因子分解机算法DE-FM在训练速度和准确性上均有所提高。

Factorization Machine Based on Differential Evolution

Factorization machine(FM) is a new machine learning algorithm based on the matrix factorization.It can be used to deal with the regression problems,classification problems and ranking problems.The solution of parameters in this model is based on the optimization method of gradient.However,under the condition of small amount of samples,the optimization method based on gradient has a slow convergence rate and may stick into local optimum.Differential evolution(DE) is a heuristic global optimization algorithm.It has a fast convergence rate.In order to improve the accuracy of FM,we proposed the DE-FM algorithm,which searches the best parameters of FM model with DE algorithm.We compared DE-FM with FM on the Diabetes dataset,the Horse-Colic dataset and the Music dataset,and the result shows that DE-FM can improve the accuracy.