  The application of machine learning and deep learning, 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 some 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 architectures, initialized randomly, and "vote" on
the decisions made by the committees as a whole. Yet, it is possible for the
committee members to "agree" on identical sets of weights and biases for all
nodes and edges. Members of these committees also cannot be expanded to
accommodate new features and entire committees must therefore be retrained in
order to do so. We propose the Random Hivemind (RH) approach, which 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 predictions
of hazardous Solar Energetic Particle (SEP) events by comparing it to that of
using both CoNNs and the aforementioned setup of committees. Our results
demonstrate that RH, while having a comparable or better performance than the
CoNN and a Committee-based approach, demonstrates a lesser score spread for the
individual experiments, and shows promising results with respect to capturing
almost every single flare instance leading to SEPs.
