Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset samples are involved in both the training and validation stages. Moreover, the results show that FOXANN outperforms traditional ANN and logistic regression methods as well as other models proposed in the literature such as ABC-ANN, ABC-MNN, CROANN, and PSO-DNN, achieving a higher accuracy of 0.9969 and a lower validation loss of 0.0028. These results demonstrate that FOXANN is more effective than traditional methods and other proposed models across standard datasets. Thus, FOXANN effectively addresses the challenges in ML algorithms and improves classification performance.

翻译：人工神经网络在机器学习中扮演着关键角色，提升其性能具有重要需求。本文提出FOXANN，这是一种新颖的分类模型，它将最新开发的Fox优化器与人工神经网络（ANN）相结合，以解决机器学习问题。Fox优化器取代了ANN中的反向传播算法，优化了突触权重，并以最小损失实现了高分类精度，同时提升了模型的泛化能力与可解释性。FOXANN的性能在三个标准数据集上进行了评估：鸢尾花数据集、威斯康星州乳腺癌数据集和葡萄酒数据集。本文展示的结果基于100个训练周期并使用10折交叉验证得出，确保了所有数据集样本均参与训练和验证阶段。此外，结果表明，FOXANN在传统ANN和逻辑回归方法以及文献中提出的其他模型（如ABC-ANN、ABC-MNN、CROANN和PSO-DNN）中表现更优，达到了0.9969的更高准确率和0.0028的更低验证损失。这些结果证明，FOXANN在标准数据集上比传统方法及其他提出的模型更为有效。因此，FOXANN有效应对了机器学习算法中的挑战，并提升了分类性能。