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带有不平衡数据的二进制分类

Jyun-You Chiang¹, Yuhlong Lio², Chien-Ya Hsu³

¹School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China.

Entropy (Basel, Switzerland)

|January 22, 2024

概括

这项研究引入了一个期望最大化 (EM) 算法,用于零膨胀的伯努利 (ZIBer) 模型,数据不平衡. 对于这些数据集,LightGBM和ZIBer模型显示了与人工神经网络 (ANN) 相比具有竞争力的预测性能.

科学领域:

统计统计统计统计
机器学习机器学习
计算统计学计算统计学

背景情况:

不平衡的数据,其特点是响应变量中过多的零计数,对二进制分类任务构成重大挑战.
当前的方法在处理零膨胀和不平衡数据集时,难以准确地估计和预测参数.

研究的目的:

提出一种预期最大化 (EM) 算法,以简化对零膨胀的伯努利 (ZIBer) 模型参数与不平衡数据的最大概率估计器 (MLEs) 的计算.
使用蒙特卡洛模拟,将ZIBer模型的预测性能与流行的机器学习算法 (如LightGBM和人工神经网络 (ANN)) 进行比较.

主要方法:

开发一个预期最大化 (EM) 算法,以有效地推导ZIBer模型参数的MLEs.
实施物流回归模型,在ZIBer框架内将伯努利概率与共变量联系起来.
使用蒙特卡洛模拟进行比较分析,以评估ZIBer,LightGBM和ANN模型的预测性能.

主要成果:

没有任何一种方法在不平衡数据上的预测性表现的所有场景中表现出一致的主导地位.
与人工神经网络 (ANN) 模型相比,零膨胀的伯努利 (ZIBer) 模型和LightGBM表现出更具竞争力的预测能力.
拟议的EM算法有效地简化了对具有不平衡数据的ZIBer模型的参数估计.

结论:

对于零膨胀不平衡数据集,ZIBer模型和LightGBM提供了强大的预测性能,在某些情况下超过了ANN.
选择不平衡二进制分类模型时,应考虑数据的特定特征,因为没有普遍的最佳方法.
开发的EM算法为ZIBer模型中的参数估计提供了一种高效的计算方法,特别有利于不平衡数据场景.

关键词:

Entropy Entropy 人工神经网络的人工神经网络预期最大化算法是指期望最大化算法.逻辑回归的逻辑回归零膨胀模型的零膨胀模型

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