Abstract: With the development of the Internet and Internet of Things technologies,data collection has become easier.However,it is necessary to reduce the dimensionality of high-dimensional data.High-dimensional data contain many redundant and unrelatedfeatures,which will increase the computational complexity of the model and even reduce the performance of the model.Feature selection can reduce the computational cost and remove redundant features by reducing feature dimensions to improve the performance of a machine learning model,and retain the original features of the data,with good interpretability.It has become one of important data preprocessing steps in machine learning.Rough set theory is an effective method which can be used to feature selection.It preserves the characteristics of the original features by removing redundant information.However,it is difficult to find the global optimal feature subset by using the traditional rough sets-based feature selection method because the cost of computing all feature subset combinations is very high.In order to overcome above problems,a feature selection method based on rough sets and improved whale optimization algorithm was proposed.An improved whale optimization algorithm was proposed by employing poli-tics of population optimization and disturbance so as to avoid local optimization.The algorithm first randomly initializes a series of feature subsets,and then uses the objective function based on the rough sets attribute dependency to evaluate the goodness of each subset.Finally,the improved whale optimization algorithm is used to find an acceptable approximate optimal feature subset by iterations.The experimental results on the UCI dataset show that the proposed algorithm can find a subset of features with less information loss and has higher classification accuracy when the support vector machine is used as the classifier for evaluation.Therefore,the proposed algorithm has a certain advantage in feature selection.

Key words: Attribute dependency, Feature selection, Improved whale optimization algorithm, Optimal feature subset, Rough set theory