Machine learning models need to be continually updated or corrected to ensure
that the prediction accuracy remains consistently high. In this study, we
consider scenarios where developers should be careful to change the prediction
results by the model correction, such as when the model is part of a complex
system or software. In such scenarios, the developers want to control the
specification of the corrections. To achieve this, the developers need to
understand which subpopulations of the inputs get inaccurate predictions by the
model. Therefore, we propose correction rule mining to acquire a comprehensive
list of rules that describe inaccurate subpopulations and how to correct them.
We also develop an efficient correction rule mining algorithm that is a
combination of frequent itemset mining and a unique pruning technique for
correction rules. We observed that the proposed algorithm found various rules
which help to collect data insufficiently learned, directly correct model
outputs, and analyze concept drift.

机器学习模型需要不断更新或校正，以确保预测准确性始终保持高水平。本研究提出了一种校正规则挖掘算法，通过组合频繁项集挖掘和独特的剪枝技术，获取描述不准确子集群和纠正方法的综合规则列表，并从中收集数据不足的信息、直接纠正模型输出和分析概念漂移。

分类模型修正的规则挖掘

Rule Mining for Correcting Classification Models

Knowing the features of a complex system that are highly relevant to a
particular target variable is of fundamental interest in many areas of science.
Existing approaches are often limited to linear settings, sometimes lack
guarantees, and in most cases, do not scale to the problem at hand, in
particular to images. We propose DRCFS, a doubly robust feature selection
method for identifying the causal features even in nonlinear and high
dimensional settings. We provide theoretical guarantees, illustrate necessary
conditions for our assumptions, and perform extensive experiments across a wide
range of simulated and semi-synthetic datasets. DRCFS significantly outperforms
existing state-of-the-art methods, selecting robust features even in
challenging highly non-linear and high-dimensional problems.

DRCFS 是一种具有理论保证的双重稳健特征选择方法，可用于确定复杂系统中与目标变量高度相关的因果特征，特别是针对非线性和高维问题。