Discovering novel features to use in machine learning techniques, such as machine learning techniques for diagnosing medical conditions

A method, performed by a computing system having at least one processor and at least one memory, for discovering features for use in machine learning, the method comprising:

• for each of a plurality of feature generators,
• for each of a plurality of sets data signals,
• extracting values from the set of data signals, and
• applying the feature generator to the extracted values to produce a feature value, and
• generating a feature vector based on the produced feature values;
• for each of a plurality of generated feature vectors,
• calculating a novelty score for the feature vector;
• identifying one or more feature generators from among the plurality of feature generators whose calculated novelty score exceeds a novelty threshold;
• and
• mutating each of the identified one or more feature generators.

A computer-readable medium storing instructions that, when executed by a computing system having at least one memory and at least one processor, cause the computing system to perform a method for discovering features for use in machine learning, the method comprising:

• for each of a plurality of feature generators,
• for each of a plurality of data signals,
• extracting values from the data signal, and
• applying the feature generator to the extracted values to produce a feature value, and
• generating a feature vector based on the produced feature values;
• for each of a plurality of pairs of feature vectors, calculating a distance between each feature vector in the pair of feature vectors;
• calculating an average distance value for a first feature vector based on the calculated distances between pairs of features vectors that include the first feature vector;
• generating a value for the first feature vector based at least in part on the calculated average distance value for the first feature vector and the calculated distances between pairs of features vectors that include the first feature vector;
• for each of a plurality of generated feature vectors,
• calculating a novelty score for the feature vector;
• identifying one or more feature generators from among the plurality of feature generators whose calculated novelty score exceeds a novelty threshold;
• and
• mutating each of the identified one or more feature generators.

A computing system comprising:

• one or more processors;
• one or more memories;
• a first component configured to, for each of a plurality of generated feature vectors,
• calculate a novelty score for the feature vector;
• a second component configured to identify one or more feature generators from among the plurality of feature generators whose calculated novelty score exceeds a novelty threshold;
• and
• a third component configured to mutate each of the identified one or more feature generators,
• wherein the first component, the second component, and the third component each comprises computer-executable instructions stored in the one or more memories for execution by the computing system.