基于互信息和文化基因算法的网络流量特征选择

利用文化基因框架的引导，提出一种结合了封装和过滤的混合型特征选择算法.该算法在传统的遗传算法中采用了基于互信息的局部搜索算法，全局搜索以分类器精度为适应度函数，保证得到全局最优解；局部搜索以联合互信息为评价指标，加快了寻找最优特征子集的收敛速度.实验表明，与现有算法相比，该算法在特征数量和计算复杂度上有显著改进，采用该算法的网络流量识别方法能以更少的特征获得更高的分类精度.

A Hybrid Feature Selection Algorithm Based on Mutual Information and Memetic Framework to Optimize Traffic Classification

Under the memetic framework， a new feature selection method combining filter and wrapper models was proposed. In the hybrid algorithm， classifier accruracy was used as fitness function to ensure global optimization， while joint mutual information was used as evaluation indicator to accelerate the process. The experimental results indicated that the proposed method outperformed the existing methods in computational efficeicecy and number of selected features. Applying this algorithm to traffic classification resulted in the improved accuracy with fewer features.