Feature selection aims to reduce the dimensionality of patterns for clas-sificatory analysis by selecting the most informative rather than irrelevant and/or redundant features. In this study, a hybrid genetic algorithm for feature selection is presented to combine the advantages of both wrappers and ...
for feature selection(MECY-FS).The MECY-FS algorithm takes advantage of genetic searching and multi-objective optimisation to overcome the limitations of greedy feature selection algorithms.MECY-FS is a hybrid approach that combines both,lter and wrapper methods to evaluate the feature subsets.During ...
Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition. In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multi-objective genetic algorithm where the minim......
In this paper, a two-stage improved gray wolf optimization (IGWO) algorithm for feature selection on high-dimensional data is proposed. In the first stage, a multilayer perceptron (MLP) network with group lasso regularization terms is first trained to construct an integer optimization problem ...
完整算法如下: 参考资料 Learn Crow Search Algorithm Step-by-Step with Example [ESWA22 - Behrouz Samieiyan] Novel optimized crow search algorithm for feature selection 发布于 2023-03-08 13:26・IP 属地江苏 内容所属专栏 机器学习 记录一些平时学习、整理的内容 订阅专栏 ...
from sklearn.feature_selection import SelectKBest, f_classif 假设X是特征矩阵，y是目标变量 X = ... 特征数据 y = ... 目标变量 使用卡方检验选择前5个最佳特征 selector = SelectKBest(score_func=f_classif, k=5)X_new = selector.fit_transform(X, y)检查所选特征的p值 print(selector...
Genetic Algorithm for Feature Selection READ ME - Instructions on how to run the code. To run the MATLAB code Step 1: Run the GA.m file You can replace the crossover, mutation, classifier, and dataset with those of your choice.
This study proposes a novel method that employs correlation based filter for dimensionality reduction followed by fuzzy rough quick reduct for feature selection on a particle swarm optimization search space. The first phase removed the redundant genes using correlation coefficient filter on a particle swa...
The new Potential Difference Algorithm for feature selection is a data pre-processing algorithm. Data preprocessing is one of the study topics in data mining. Normally, raw data is just a collection of nonsense numbers. The decision could not make based on the raw data. The algorithms related ...
29,42 introduced a Quantum Whale Optimization Algorithm for feature selection problems. It represents population individuals using quantum bits and incorporates mutation operators, improved mutations, and crossover operators. Statistical tests showed that the improved algorithm outperforms other metaheuristic ...