计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220600241-6.doi: 10.11896/jsjkx.220600241
黄宇航, 宋友, 王宝会
HUANG Yuhang, SONG You, WANG Baohui
摘要: 信用评估是金融领域的一个关键问题,它可以预测出一个用户是否存在拖欠风险,从而减少坏账损失。信用评估的关键挑战之一就是数据集存在着大量无效或冗余特征。为了解决该问题,提出了一种改进的森林优化特征选择算法(Improved Feature Selection using Forest Optimization Algorithm,IFSFOA)。该算法针对原始算法FSFOA的不足,在初始化阶段使用基于卡方校验的初始化策略代替随机化初始,提升算法寻优的能力;在局部播种阶段利用多层级变异策略,优化局部搜索能力,解决FSFOA的搜索空间受限和局部性问题;在更新候选森林时,使用贪婪选取策略挑选优质树,淘汰劣质树,收敛搜索发散过程。最后在涵盖了低维、中维和高维的公开信用评估数据集上设置对比实验,结果表明IFSFOA在分类和维度缩减方面的能力的综合表现均优于FSFOA和近年提出的较为高效的特征选择算法,验证了IFSFOA的有效性。
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