受肝脏排毒机制启发，新模型在多个数据集上实现高准确率且过拟合更少。
Artificial Liver Classifier: A New Alternative to Conventional Machine Learning Models
- 模仿人体肝脏的排毒机制设计，用简单数学运算完成分类。
- 在鸢尾花数据集达100%准确率，乳腺癌数据集99.12%，优于多种传统模型。
- 适合追求高效、低过拟合的分类任务，尤其适合生物启发型算法研究者。
监督学习分类器常面临性能不佳、准确率低或过拟合问题。本文提出一种受人体肝脏解毒功能启发的新模型——人工肝脏分类器（Artificial Liver Classifier, ALC），具备结构简单、计算快速、抗过拟合能力强及多分类效果好等优势，通过基础数学运算实现分类。为优化参数，引入改进的狐狸优化算法（IFOX）。在五个基准数据集（Iris Flower、Breast Cancer Wisconsin、Wine、Voice Gender、MNIST）上评估，ALC在Iris数据集上达到100%准确率，超越逻辑回归、多层感知机和支持向量机；在乳腺癌数据集上达99.12%准确率，优于XGBoost和逻辑回归。所有数据集上，ALC均表现出更小的泛化差距与更低损失值。结果表明，生物启发模型有望推动高效机器学习分类器的发展，为该领域开辟新方向。
原文摘要 · Abstract (English)
Supervised machine learning classifiers sometimes face challenges related to the performance, accuracy, or overfitting. This paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning model inspired by the human liver's detoxification function. The ALC is characterized by its simplicity, speed, capability to reduce overfitting, and effectiveness in addressing multi-class classification problems through straightforward mathematical operations. To optimize the ALC's parameters, an improved FOX optimization algorithm (IFOX) is employed during training. We evaluate the proposed ALC on five benchmark datasets: Iris Flower, Breast Cancer Wisconsin, Wine, Voice Gender, and MNIST. The results demonstrate competitive performance, with ALC achieving up to 100\% accuracy on the Iris dataset--surpassing logistic regression, multilayer perceptron, and support vector machine--and 99.12\% accuracy on the Breast Cancer dataset, outperforming XGBoost and logistic regression. Across all datasets, ALC consistently shows smaller generalization gaps and lower loss values compared to conventional classifiers. These findings highlight the potential of biologically inspired models to develop efficient machine learning classifiers and open new avenues for innovation in the field.
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