Idea: a data classification metamodel based on the immune system: train a small bag of classifiers and clone the ones that perform well, but with a small chance of random mutations to the hyperparameters. Weight classifiers created in this manner exponentially based on iterations since last correct classification. Keep a “memory threshold” below which the weight will not fall in case that pattern is encountered again.