基于文化鲸鱼优化算法的特征权重优化分配方法

Method of Feature Weight Optimization Allocation Based on Cultural Whale Optimization Algorithm

为了解决基于数据的预测模型中特征权重分配不合理的问题,将鲸鱼优化算法(whale optimization algorithm,WOA)纳入文化算法的种群空间中,获得了一种文化鲸鱼优化算法(cultural whale optimization algorithm,CWOA)以用于特征权重的优化分配.首先,将预测模型的均方根误差作为适应度函数;然后,采用WOA在种群空间中对特征权重进行迭代寻优;接着,通过接受函数将种群空间中的最优权重置于信仰空间中进行性能评价与双变异演化,以此形成形势知识和规范知识;最后,通过影响函数对种群空间中的权重进行更新指导,如此循环,从而得到特征权重的优化分配结果.以基于案例推理的预测模型为例,使用加州大学欧文分校(University of California Irvine,UCI)标准数据集对特征权重的不同分配方法进行了对比实验,结果表明该方法分配权重后的预测精度最优,在涉及特征权重分配的机器学习领域具有一定应用价值.

To solve the problem of unreasonable distribution of feature weights in the data-based prediction model,the whale optimization algorithm was included in the population space of cultural algorithm,and a cultural whale optimization algorithm(CWOA)was acquired for optimal distribution of feature weights.First,the root mean square error of the prediction model was used as the fitness function,and the whale optimization algorithm was utilized to iteratively refine the feature weight in the population space.Then,the optimal weight in the population space was placed in the belief space through the defined acceptance function for performance evaluation and double mutation evolution so as to form situational knowledge and normative knowledge.Finally,the influence function was used to update and guide the weights in the population space to further obtain the optimal allocation results of feature weights.By taking the prediction model on the basis of case-based reasoning as an example,University of California Irvine(UCI)standard data set was used to conduct the contract experiments on different distribution techniques for feature weights.Results show that the prediction accuracy of the technique mentioned in this paper after weight distribution is optimal,delivering certain application value in the field of machine learning involving the distribution of feature weights.