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

TL;DR

This paper presents the Artificial Liver Classifier (ALC), a biologically inspired supervised learning model that offers high accuracy, reduced overfitting, and computational efficiency, validated on multiple benchmark datasets.

Contribution

The paper introduces the ALC model and the IFOX optimization algorithm, demonstrating superior performance over traditional classifiers on standard datasets.

Findings

ALC achieves 100% accuracy on Iris dataset, surpassing traditional models.

ALC outperforms XGBoost and logistic regression on Breast Cancer dataset.

ALC shows smaller generalization gaps and lower loss values across datasets.

Abstract

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.