摘要:在分类中，特征选择一直是一个重要而又困难的问题.最近的研究表明，森林优化特征选择算法（FSFOA）具有更好的分类性能及较好的维度缩减能力.然而，初始化阶段的随机性、更新机制上的局限性及局部播种阶段新树的劣质性严重限制了该算法的分类性能和维度缩减能力.该文采用一种新的初始化策略和更新机制，并在局部播种阶段加入贪婪策略，形成特征选择算法IFSFOA，在最大化分类性能的同时，最小化特征个数.实验阶段，IFSFOA使用SVM，J48和KNN分类器指导学习过程，通过机器学习数据库UCI上的小维、中维、高维数据集进行测试.实验结果表明：与FSFOA相比，IFSFOA在分类性能和维度缩减上均有明显提高.将IFSFOA算法与近几年提出的比较高效的特征选择方法进行对比，不论是在准确率，还是在维度缩减上，IFSFOA仍具有很强的竞争力.



Abstract:In classification, feature selection has been an important, but difficult problem. Recent research results disclosed that feature selection using forest optimization algorithm (FSFOA) has a better classification performance and good dimensionality reduction ability. However, the randomness of initialization phase, the limitations of updating mechanism and the inferior quality of the new tree in the local seeding stage severely limit the classification performance and dimensionality reduction ability of the algorithm. In this paper, a new initialization strategy and updating mechanism are used and a greedy strategy is added in the local seeding stage to form a new feature selection algorithm (IFSFOA) in order to maximize the classification performance and simultaneously minimize the number of features. In experiment, IFSFOA uses SVM, J48 and KNN classifiers to guide the learning process while utilizing the machine learning database UCI for testing. The results show that compared with FSFOA, IFSFOA has a significant improvement in classification performance and dimensionality reduction. Comparing IFSFOA algorithm with more efficient feature selection methods proposed in recent years, IFSFOA is still very competitive in both accuracy and dimensionality reduction.



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