基于构造性神经网络与全局密度信息的不平衡数据欠采样方法  

Imbalanced Undersampling Based on Constructive Neural Network and Global Density Information

作　　者：严远亭[1] 马迎澳 任艳平 张燕平[1] YAN Yuanting;MA Yingao;REN Yanping;ZHANG Yanping(College of Computer Science and Technology,Anhui University,Hefei 230601,China)

机构地区：[1]安徽大学计算机科学与技术学院,合肥230601

出　　处：《计算机科学》2023年第10期48-58,共11页Computer Science

基　　金：国家自然科学基金(61806002)。

摘　　要：多数类欠采样是当前数据层面解决不平衡数据学习的主流技术之一,近年来,研究者们提出了一系列的欠采样方法,但大多都将重点放在如何选择代表性的样本,从而降低信息损失。然而,如何在欠采样过程中保持多数类内部的结构信息,仍然是欠采样面临的主要挑战。针对该挑战,提出了一种基于构造性神经网络和全局分布密度的不平衡数据集欠采样方法。该方法首先基于构造性神经网络,设计了一种多数类局部模式的学习方法;然后基于多数类局部模式,设计了两种具有结构保持特性的样本选择策略;最后针对局部模式学习的随机性可能导致的采样结果非优的问题,进一步引入了bagging集成策略,提升了方法的性能。在59个数据集上与13种对比方法进行了对比实验,验证了所提方法在G-mean,AUC和F1-score这3个常用指标上的有效性。Undersampling is one of the mainstream data-level technologies to deal with imbalanced data.In recent years,researchers have proposed numerous undersampling methods,but most of them focus on how to select representative majority class samples to avoid the loss of informative data.However,how to maintain the structures of the original majority class in the process of undersampling is still an open challenge.To this end,an undersampling method for imbalanced data classification is proposed based on constructive neural network and data density.Firstly,it detects the majority local patterns with a simplified constructive process.Then,two sample selection strategies are designed to maintain the structure of the selected groups according to the original majority distribution information.Finally,to solve the problem that the randomness of local pattern learning may lead to non-optimal sampling results,the bagging technique is introduced to further improve the learning performance.Comparative experiments with 13 comparison methodson 59 datasets verify the effectiveness of the proposed method in terms of three metrics G-mean,AUC and F1-score.

关 键 词：欠采样 不平衡数据 分布密度 构造性神经网络 集成学习 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]