# Artificial Liver Classifier: A New Alternative to Conventional Machine Learning Models

arXiv: https://arxiv.org/abs/2501.08074 · HTML: https://mlsanity.com/paper/2501.08074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis](https://mlsanity.com/paper/2105.01650/) (2021)
- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [Deep reinforcement learning for process design: Review and perspective](https://mlsanity.com/paper/2308.07822/) (2023)
- [Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process](https://mlsanity.com/paper/1904.05226/) (2019)
- [GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond](https://mlsanity.com/paper/2307.10420/) (2023)
- [FOXANN: A Method for Boosting Neural Network Performance](https://mlsanity.com/paper/2407.03369/) (2024)

## Abstract (arXiv)

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.