混合改进的花授粉算法与灰狼算法用于特征选择

Hybrid Improved Flower Pollination Algorithm and Gray Wolf Algorithm for Feature Selection

特征选择在数据预处理阶段中极为重要。特征选择的优劣不仅影响着神经网络训练的时间长短,更影响神经网络性能的好坏。灰狼改进花授粉算法(Grey Wolf Improved Flower Pollination Algorithm,GIFPA)是一种基于花授粉算法(Flower Pollination Algorithm,FPA)框架与灰狼优化算法融合的混合算法,将其应用于特征选择问题,既可以保留原始特征的内涵信息,又可以最大化分类特征的准确率。GIFPA算法在花授粉算法的异花授粉阶段中加入了最差个体信息,并用作全局搜索,将灰狼优化算法中的狩猎过程作为局部搜索,并且通过转换系数来调节二者的搜索过程。同时,为了克服群智能算法易陷入局部最优的问题,首次采用数据挖掘领域中的RelifF算法,通过RelifF算法过滤出高权重特征并用于改进最佳个体信息。为了验证算法的性能,实验选取UCI数据库中21个领域的经典数据集进行测试,利用K近邻(KNN)分类器进行分类测评,以适应度值和准确率作为评价标准,并通过K-折交叉验证来克服过拟合问题。实验选择了包括FPA算法在内的多种经典算法和先进算法进行比较,结果表明GIFPA算法在特征选择问题上有很强的竞争力。

Feature selection is very important in the stage of data preprocessing.The quality of feature selection not only affects the training time of the neural network but also affects the performance of the neural network.Grey Wolf improved Flower pollination algorithm(Grey Wolf improved Flower pollination algorithm,GIFPA)is a hybrid algorithm based on the fusion of flower pollination algorithm framework and gray wolf optimization algorithm.When it is applied to feature selection,it can not only retain the connotation information of the original features but also maximize the accuracy of classification features.The GIFPA algorithm adds the worst individual information to the FPA algorithm,uses the cross-pollination stage of the FPA algorithm as the global search,uses the hunting process of the gray wolf optimization algorithm as the local search,and adjusts the search process of the two through the conversion coefficient.At the same time,to overcome the problem that swarms intelligence algorithm is easy to fall into local optimization,this paper uses the RelifF algorithm in the field of data mining to improve this problem and uses the RelifF algorithm to filter out high weight features and improve the best individual information.To verify the performance of the algorithm,21 classical data sets in the UCI database are selected for testing,k-nearest neighbor(KNN)classifier is used for classification and evaluation,fitness value and accuracy are used as evaluation criteria,and K-fold crossover verification is used to overcome the over-fitting problem.In the experiment,a variety of classical algorithms and advanced algorithms,including the FPA algorithm,are compared.The experimental results show that the GIFPA algorithm has strong competitiveness in feature selection.