Comparison of activation function on extreme learning machine (ELM) performance for classifying the active compound

Abstract

The active compound could interact with other molecules and can lead to a variety of positive and negative effects on the living system. Therefore, classification of the compound is very important for understanding its character and functions for human medicine. The manual classification of compound functions by laboratory test is time and cost consuming. Based on the previous research, the functions of the compound could be predicted based on its molecular structure in the format of the Simplified Molecular Input Line Entry System (SMILES). So, we employed the Extreme Learning Machine (ELM) for classifying the active compounds according to its SMILES structure. The result of this study suggested that ELM could classify the active compounds very fast and guarantee optimal performance. The accuracy and computational time of classification model were depending on the activation function. This experiment uses eleven activation functions i.e Binary Step Function, Sigmoid, Swish, Exponential Linear Squashing (ELiSH), Hyperbolic Tangent(TanH), Hard Hiperbolic Function (HardTanH), Rectified Linear Unit (ReLU), TanhRe, Exponential Linier Units (ELUs), SoftPlus, and Leaky ReLU (LReLU). The results of experiments show that ELU and TanHRe have the best performance based on average and maximal accuracy. Accuracy of the system depends on the patterns in class and the activation functions which are used. Based on experimental results, the average accuracy can reach 80.56% on ELUs activation function and the maximum accuracy 88.73% on TanHRe.