Integrated feature and parameter optimization for an evolving spiking neural network: exploring heterogeneous probabilistic models.
This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
Schliebs
Stefan
S
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand. sschlieb@aut.ac.nz
Defoin-Platel
Michaël
M
Worner
Sue
S
Kasabov
Nikola
N
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D033622:Ceratitis capitata; D016208:Databases, Factual; D017753:Ecosystem; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D016011:Normal Distribution; D012984:Software; D013997:Time Factors