基于广义重要度和runner-root算法的特征选择

特征选择是机器学习、模式识别和数据挖掘等领域数据预处理阶段的重要步骤.现实中采集的数据维度很高,存在大量冗余和噪声数据,这使得计算时间增加的同时还会对建模结果产生误导性.结合属性子集的广义重要度和智能优化runner-root算法提出一种特征选择算法,用runner-root算法进行迭代寻优,用属性子集的广义重要度和所...

Feature selection based on general importance and runner-root algorithm

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.