用合作博弈同时融合多种评估指标，提升多分类集成模型性能
A Cooperative Game-Based Multi-Criteria Weighted Ensemble Approach for Multi-Class Classification
- 基于合作博弈框架，综合多维度信息动态分配分类器权重
- 在Open-ML-CC18数据集上超越现有加权集成方法，准确率显著提升
- 适合处理类别不平衡、过拟合等复杂场景的多分类任务
第四次工业革命以来，人工智能技术广泛应用，但仍面临过拟合/欠拟合、类别不平衡及模型表征能力受限等问题。集成学习通过模型组合可缓解上述挑战。现有投票集成方法虽采用不同加权策略并取得性能提升，但多数仅基于单一评价标准确定权重，难以全面反映分类器的真实特性。为此，本文提出一种基于合作博弈的多准则加权集成方法，能够同时考虑多种先验信息，在多准则环境下实现更合理的权重分配。实验在Open-ML-CC18数据集上进行，结果表明该方法性能优于现有加权集成方法。
原文摘要 · Abstract (English)
Since the Fourth Industrial Revolution, AI technology has been widely used in many fields, but there are several limitations that need to be overcome, including overfitting/underfitting, class imbalance, and the limitations of representation (hypothesis space) due to the characteristics of different models. As a method to overcome these problems, ensemble, commonly known as model combining, is being extensively used in the field of machine learning. Among ensemble learning methods, voting ensembles have been studied with various weighting methods, showing performance improvements. However, the existing methods that reflect the pre-information of classifiers in weights consider only one evaluation criterion, which limits the reflection of various information that should be considered in a model realistically. Therefore, this paper proposes a method of making decisions considering various information through cooperative games in multi-criteria situations. Using this method, various types of information known beforehand in classifiers can be simultaneously considered and reflected, leading to appropriate weight distribution and performance improvement. The machine learning algorithms were applied to the Open-ML-CC18 dataset and compared with existing ensemble weighting methods. The experimental results showed superior performance compared to other weighting methods.
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