Feature selection method based on LightGBM and ant colony optimization
When processing high-dimensional feature data,there are usually issues of redundancy and irrelevance.As a tradi-tional feature selection algorithm,Relief is widely used due to its high stability and computational efficiency.However,the feature selection results are random,and for datasets with strong dependencies between features,such as collinearity,it may lead to inaccu-rate results.Based on the research on feature selection methods,an L-ACO method based on LightGBM and ant colony algorithm was proposed.The heuristic information of the L-ACO algorithm ant colony path search process was represented by the feature im-portance of LightGBM algorithm.At the same time,the Pearson correlation coefficient between features is used to adjust the concen-tration of pheromones in order to better control the correlation of features.Experiments have shown that the L-ACO method can re-duce the number of features,reduce feature redundancy,and improve algorithm performance while ensuring classification accuracy.
NETL
NSTL
万方数据
维普
