A Method to Improve Speed of Training Algorithm in Artificial Hydrocarbon Networks

Artificial hydrocarbon networks (AHN) is a supervised machine learning method inspired on chemical carbon networks that simulate heuristic chemical rules involved within organic molecules to represent the structure and behavior of data. However, training AHN depends on a relevant number of parameters. In that sense, the original training algorithm presents some issues to find suitable parameters in a reasonable amount of time. Thus, this paper proposes a new training algorithm for AHN based on the concept of extreme learning machines, to update weight parameters related to the molecular functions. To evaluate the effectiveness of the proposed algorithm, binary classification and regression tests are performed over real public datasets from a central data repository specialized in machine learning problems. The results obtained validated that the updating of the weight parameters using the new training algorithm in the molecular structures is efficient and maintains the expected results of model accuracy. In addition, this work increased up to 24.88% the speed of the training phase in contrast to the original algorithm.