Machine learning system to identify and optimize features based on historical data, known patterns, or emerging patterns

A system comprising:

• a first interface configured to receive (i) historical data and (ii) current event data;
• a graph module configured to update a graph comprising nodes and edges, wherein each node corresponds to an entity type, and wherein each edge represents a relationship between two nodes;
• and
• a supervised machine learning engine programmed to perform steps comprising:
• training a machine learning model of the supervised machine learning engine using a corpus, wherein the corpus comprises a training data and a test data;
• classifying a plurality of nodes in the graph based on a known pattern and the machine learning model, by setting a classification attribute of each node to one of a plurality of classifications;
• detecting, by the supervised machine learning engine, an emerging pattern between a first node and second node of the plurality of nodes in the graph based on at least the (i) and (ii);
• inserting an edge between the first node and the second node in the graph in response to the detecting of the emerging pattern;
• and
• adjusting a feature vector of the supervised machine learning engine based on an objective function to minimize a loss function.

A non-transitory computer readable medium storing computer-executable instructions that, when executed by a processor, cause a machine learning engine to:

• train a machine learning model of the machine learning engine using a corpus, wherein the corpus comprises a training data and a test data;
• classify a plurality of nodes in a graph stored in computer memory based on known patterns and the machine learning model, by setting a classification attribute of each node in the graph to one of a plurality of classifications;
• detect, by the machine learning engine, an emerging pattern between a first node and second node in the graph based on historical data and current event data;
• insert an edge between the first node and the second node in the graph in response to the detecting of the emerging pattern;
• and
• adjust a feature vector of the machine learning engine based on an objective function to minimize a loss function.