Predicting SEP All-Clear Periods With Random Hivemind Classifiers

The application of machine learning and deep learning techniques, including the wide use of non-ensemble, conventional neural networks (CoNN), for predicting various phenomena has become very popular in recent years thanks to the efficiencies and the abilities of these techniques to find relationships in data without human intervention. However, certain CoNN setups may not work on all datasets, especially if the parameters passed to it, including model parameters and hyperparameters, are arguably arbitrary in nature and need to continuously be updated with the need to retrain the model. This concern can be partially alleviated by employing committees of neural networks that are identical in terms of input features and initialized randomly and “vote” on the decisions made by the committees as a whole. Yet, members of the committee have similar architectures and features passed to them, making it possible for the committee members to “agree” blindly. The random hivemind (RH) helps to alleviate this concern by having multiple neural network estimators make decisions based on random permutations of features and prescribing a method to determine the weight of the decision of each individual estimator. The effectiveness of RH is demonstrated through experimentation in the “all-clear” prediction of SEPs by comparing it to that of using both CoNNs and the aforementioned setup of committees identical in input features in this application.