Artificial Liver Classifier: A New Alternative to Conventional Machine Learning Models
This work addresses performance and overfitting issues in machine learning classifiers, offering a biologically inspired alternative that is incremental in nature.
The paper tackled challenges in supervised machine learning classifiers by introducing the Artificial Liver Classifier (ALC), a biologically inspired model that achieved up to 100% accuracy on the Iris dataset and 99.12% on the Breast Cancer dataset, outperforming conventional methods like logistic regression and XGBoost.
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.