基于SHO-SA算法的案例推理预测模型特征权重优化

Feature Weights Optimization Based on SHO-SA Algorithm for Case-based Reasoning Prediction Model

针对案例推理(case-based reasoning, CBR)检索过程中特征权重的分配结果直接影响CBR预测模型性能的问题,提出了一种基于自私牧群优化-模拟退火(selfish herd optimizer-simulated annealing, SHO-SA)算法的特征权重优化分配方法.首先,将CBR预测模型的均方根误差定义为SHO算法和SA算法中权重寻优的适应度;然后,通过SHO算法的牧群运动、捕食及恢复等步骤得到种群内最小均方根误差所对应的权重;最后,采用SA算法对上述权重进行随机搜索,从而获得特征权重的近似最优解.采用加州大学欧文分校(University of California Irvine, UCI)数据集中的5个标准回归数据集进行实验,结果表明该方法与一些典型的优化方法相比可以显著提高CBR预测模型的精度,说明SA算法能够改善SHO算法陷入局部最优的问题.

Performance of the case-based reasoning(CBR) prediction model is directly affected by feature weight allocation in the retrieval process.A method based on selfish herd optimizer-simulated annealing(SHO-SA) algorithm was proposed to calculate the feature weights.The root mean square error(RMSE) of the CBR prediction model was first defined as the fitness function in the SHO algorithm and the SA algorithm to evaluate the rationality of the weight distribution.Then,the weights distribution with the minimum RMSE in the population were obtained through the herd movement,predation and recovery steps of the SHO.Finally,SA algorithm was employed to search randomly based on the above weight,and an approximate optimal solution of the feature weights was obtained.Performance evaluation was carried out by five benchmark regression datasets from University of California Irvine(UCI) datasets.Results show that compared with other typical optimization methods,the proposed method can improve the accuracy of the CBR prediction model significantly.Meanwhile,it illustrates that the matter of SHO suffering from local minima can be mended by SA algorithm.