Improving Kanice Mini: A Study on Hyperparameter Tuning and Optimization

Abstract This paper investigates and extends the capabilities of KANICE Mini, a hybrid neural architecture that integrates the Kolmogorov – Arnold Network framework with Interactive Convolution Elements. While the original implementation achieved 99.35% accuracy on the MNIST dataset, we improve the model’s performance through a refined training pipeline, enhanced regularization techniques, and structured hyperparameter optimization. Our optimized KANICE Mini achieves 99.56% accuracy on MNIST, surpassing the original result. Furthermore, we evaluate its generalization capability on more complex real-world data by applying it to the Invasive Ductal Carcinoma classification task, where it reaches 85.79% accuracy. These results demonstrate that, with careful tuning, KANICE Mini can rival significantly larger architectures in performancewhile preserving advantages in efficiency, modularity, and interpretability.