The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously. In this work, a new wrapper feature selection approach is proposed based on Whale Optimization Algorithm (WOA). WOA is a newly proposed algorithm ...
from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression from sklearn.datasets import load_iris # 加载数据 iris = load_iris() X = iris.data y = iris.target # 初始化基模型和要选择的特征数量 estimator = LogisticRegression() n_features_to_select = 2 # 使...
展开更多 As a novel granular computing model developed in recent years,sequential three-way decision has been widely used in many fields due to enormous advantages in cost-sensitive problems.In order to decrease the classification cost of traditional static classifier,the idea o...MORE 作者 张璐...
In addition, the experiments were divided into three parts: (1) in which the S-C4.5-SMOTE proposed in this paper was validated in the experiments, (2) in which the Bagging C4.5 model was trained by the Wrapper feature selection method, and (3) which compared the Bagging C4.5 algorithm ...
FS : feature selection model ( It contains several results ) sf : index of selected features ff : selected features nf : number of selected features c : convergence curve t : computational time (s) Notation Some methods have their specific parameters ( example: PSO, GA, DE ), and if ...
Ramjee, S., Gamal, A.E.: Efficient wrapper feature selection using autoencoder and model based elimination. arXiv preprintarXiv:1905.11592(2019) Scherer, M., Smolag, J., Gaweda, A.: Predicting success of bank direct marketing by neuro-fuzzy systems. In: Rutkowski, L., Korytkowski, M....
1(School of Economics and Management,Southwest Jiaotong University,Chengdu 610031 .China)2(School of Economic Information Engineering, Southwestern University of Finance and Economics’Chengdu 611130, China)Abstract ：As a novel granular computing model developed in recent years,sequential three-way decision...
fromsklearn.feature_selectionimportSelectFromModelfromsklearn.ensembleimportRandomForestClassifier as RFC RFC_= RFC(n_estimators =10,random_state=0) X_embedded= SelectFromModel(RFC_,threshold=0.005).fit_transform(X,y)#在这里我只想取出来有限的特征。0.005这个阈值对于有780个特征的数据来说,是非常高的...
Financial distress prediction (FDP) is a complex task involving both feature selection and model construction. While many studies have addressed these challenges individually, there is a lack of FDP models that integrate feature selection into the overall model building process. To address the issues ...
1) wrapper model 绕封模型 1. A feature selection method based onwrapper modelwas proposed. 针对此问题,提出使用基于绕封模型的故障特征选择方法,它采用遗传算法对特征集寻优,样本划分法进行错误率预测估计和BP神经网络学习算法进行分类。 2) wrapper method ...