Abstract: Feature selection is an important step in the data preprocessing stage in machine learning, pattern recognition, data mining and other fields. In reality, the data information collected is of high dimension, and there are redundant data and noisy data, which will increase the calculation time and mislead the modeling results at the same time. Combined with the generalized importance of attribute subsets and the intelligent optimization runner-root algorithm, a feature selection algorithm  is proposed. The method uses the runner-root algorithm for iterative optimization, and uses the generalized importance of attribute subsets and the size of the selected feature subsets as fitness functions to evaluate the selected feature subsets, so that the features that are important for decision making are searched out as far as possible in the entire sample space. The experimental results show that the proposed feature selection algorithm  can select effective feature subsets and obtain higher accuracy on the classification model. 

Key words: intelligent optimization, general importance, runner-root algorithm, feature selection