Modifying a trained classification model

A non-transitory computer readable storage medium having computer readable program code stored thereon for causing a computer system to perform a method for modifying a trained classification model, the method comprising:

• receiving feature data extracted from sensor data;
• classifying the feature data according to the trained classification model to identify a label corresponding to the feature data, wherein the trained classification model comprises a decision tree comprising a plurality of decision nodes for feature identification for a plurality of features;
• tracking identified features of the plurality of features over a predetermined amount of time;
• and
• responsive to determining that a feature of the trained classification model does not satisfy a frequency of usage threshold over the predetermined amount of time, deactivating a decision node of the decision tree of the trained classification model corresponding to the feature such that a subsequent instance of the classifying does not consider a deactivated decision node for subsequently received feature data.

An electronic device comprising:

• at least one sensor;
• a memory;
• and
• a processor configured to:
• receive sensor data from the at least one sensor;
• extract feature data from the sensor data;
• classify the feature data according to a trained classification model to identify a label corresponding to the feature data, wherein the trained classification model comprises a decision tree comprising a plurality of decision nodes for feature identification for a plurality of features;
• track identified features of the plurality of features over a predetermined amount of time;
• and
• deactivate a decision node of the decision tree of the trained classification model corresponding to a feature responsive to determining that the feature of the trained classification model does not satisfy a frequency of usage threshold over the predetermined amount of time, such that the trained classification model does not consider deactivated decision nodes for subsequently received feature data.