Computer systems and methods for feature selection for machine-learning models

A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to:

• identify an initial set of features;
• obtain an input dataset comprising a set of data records that each includes respective values for the initial set of features;
• build a feature graph based on the input dataset, wherein the feature graph comprises (i) nodes that represent the features of initial set of features and (ii) connections that represent similarities between the features of the initial set of features;
• determine a reduced set of features from the initial set of features by selecting features for inclusion in the reduced set of features based on a balancing between (i) diffusion size of features within the feature graph and (ii) diffusion overlap of features within the feature graph relative to any features that were selected for inclusion in the reduced set of features;
• and
• utilize the reduced set of features in a machine-learning process for training a machine-learning model.

A method carried out by a computing platform, the method comprising:

• identifying an initial set of features;
• obtaining an input dataset comprising a set of data records that each includes respective values for the initial set of features;
• building a feature graph based on the input dataset, wherein the feature graph comprises (i) nodes that represent the features of initial set of features and (ii) connections that represent similarities between the features of the initial set of features;
• determining a reduced set of features from the initial set of features by selecting features for inclusion in the reduced set of features based on a balancing between (i) diffusion size of features within the feature graph and (ii) diffusion overlap of features within the feature graph relative to any features that were selected for inclusion in the reduced set of features;
• and
• utilizing the reduced set of features in a machine-learning process for training a machine-learning model.