SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification
This work addresses the domain-specific problem of exoplanet habitability classification for astroinformatics researchers, presenting an incremental improvement with a new activation function.
The authors tackled the problem of classifying exoplanets into habitability classes by introducing a novel activation function called SBAF for artificial neural networks, which is derived from advanced calculus and avoids local oscillation issues.
We explore the efficacy of using a novel activation function in Artificial Neural Networks (ANN) in characterizing exoplanets into different classes. We call this Saha-Bora Activation Function (SBAF) as the motivation is derived from long standing understanding of using advanced calculus in modeling habitability score of Exoplanets. The function is demonstrated to possess nice analytical properties and doesn't seem to suffer from local oscillation problems. The manuscript presents the analytical properties of the activation function and the architecture implemented on the function. Keywords: Astroinformatics, Machine Learning, Exoplanets, ANN, Activation Function.