Superior scoring rules for probabilistic evaluation of single-label multi-class classification tasks

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rouhollah Ahmadian , Mehdi Ghatee , Johan Wahlström
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引用次数: 0

Abstract

This study introduces novel superior scoring rules called Penalized Brier Score (PBS) and Penalized Logarithmic Loss (PLL) to improve model evaluation for probabilistic classification. Traditional scoring rules like Brier Score and Logarithmic Loss sometimes assign better scores to misclassifications in comparison with correct classifications. This discrepancy from the actual preference for rewarding correct classifications can lead to suboptimal model selection. By integrating penalties for misclassifications, PBS and PLL modify traditional proper scoring rules to consistently assign better scores to correct predictions. Formal proofs demonstrate that PBS and PLL satisfy strictly proper scoring rule properties while also preferentially rewarding accurate classifications. Experiments showcase the benefits of using PBS and PLL for model selection, model checkpointing, and early stopping. PBS exhibits a higher negative correlation with the F1 score compared to the Brier Score during training. Thus, PBS more effectively identifies optimal checkpoints and early stopping points, leading to improved F1 scores. Comparative analysis verifies models selected by PBS and PLL achieve superior F1 scores. Therefore, PBS and PLL address the gap between uncertainty quantification and accuracy maximization by encapsulating both proper scoring principles and explicit preference for true classifications. The proposed metrics can enhance model evaluation and selection for reliable probabilistic classification.

Abstract Image

单标签多类分类任务概率评价的优等评分规则
为了改进概率分类模型的评估,本研究引入了罚Brier评分(PBS)和罚对数损失(PLL)两种新的高级评分规则。传统的评分规则,如Brier Score和Logarithmic Loss,有时会比正确的分类给错误的分类更高的分数。这种与奖励正确分类的实际偏好的差异可能导致次优模型选择。通过整合对错误分类的惩罚,PBS和PLL修改了传统的正确评分规则,以始终为正确的预测分配更好的分数。形式证明表明PBS和PLL在优先奖励准确分类的同时满足严格适当的评分规则性质。实验展示了使用PBS和PLL进行模型选择、模型检查点和早期停止的好处。训练时PBS与F1分数的负相关高于Brier分数。因此,PBS更有效地识别最佳检查点和早期停止点,从而提高F1分数。通过对比分析,验证了PBS和PLL选择的模型获得了更优的F1分数。因此,PBS和PLL通过封装适当的评分原则和对真实分类的明确偏好,解决了不确定性量化和准确性最大化之间的差距。所提出的度量可以增强模型的评估和选择,从而实现可靠的概率分类。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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