m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn
This is an incremental improvement for users of scikit-learn seeking efficient functions for SVM and MLP in classification tasks.
The paper introduced the m-arcsinh function as a kernel and activation function for SVM and MLP in scikit-learn, showing competitive classification performance and improvements in reliability and convergence speed on 15 datasets.
This paper describes the 'm-arcsinh', a modified ('m-') version of the inverse hyperbolic sine function ('arcsinh'). Kernel and activation functions enable Machine Learning (ML)-based algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), to learn from data in a supervised manner. m-arcsinh, implemented in the open source Python library 'scikit-learn', is hereby presented as an efficient and reliable kernel and activation function for SVM and MLP respectively. Improvements in reliability and speed to convergence in classification tasks on fifteen (N = 15) datasets available from scikit-learn and the University California Irvine (UCI) Machine Learning repository are discussed. Experimental results demonstrate the overall competitive classification performance of both SVM and MLP, achieved via the proposed function. This function is compared to gold standard kernel and activation functions, demonstrating its overall competitive reliability regardless of the complexity of the classification tasks involved.