4 results about "Decision tree learning" patented technology

System (10) and method for computer-aided pattern recognition and sequential-adaptive question generation based on user responses (22) to data-based classification questions (23) for a compatibility check between user data and reference datasets, the system comprising: a data input interface (1) that transmits the user responses (22) to all data-based classification questions (23) and a classification result (24a, 24b, 24c) resulting from all user responses (22) and belonging to these user responses (22); a processor unit (2) that transforms the user responses (22) into data points (D), wherein the data points (D) take on values ​​in a predefined range; receives the data points (D) and the classification result (24a, 24b, 24c) belonging to each of these data points (D);in a training phase of the system (10) a decision tree learning-based machine learning model (3) that automatically induces decision trees (20) from the data points (D), executes (...);

The method for transient voltage stability rule mining based on shapelet and inclined decision tree learning first mines a plurality of key local dynamic features related to a stable state from transient voltage time sequence samples to form a shapelet set with interpretability; then, constructs sample feature representation based on the shapelet set, and generates transient voltage stability decision rules by using inclined decision tree learning of a hierarchical decision relationship corresponding to the key time sequence features; finally, extracts, expresses and analyzes the generated decision rules in combination with the shapelet set and the inclined decision tree decision path to obtain key time sequence dynamic features related to transient voltage stability state and decision basis thereof. The method can extract key time sequence features with physical interpretability from transient voltage time sequence responses, mine clear transient voltage stability decision rules, and provide support for power system transient voltage stability mechanism analysis and auxiliary decision making.

A secret decision tree test device configured to evaluate a division condition at each of a plurality of nodes of a decision tree when learning of the decision tree is performed by secret calculation, the secret decision tree test device includes a memory; and a processor configured to execute inputting a numerical attribute value vector composed of specific numerical attribute values of items of data included in a data set for learning of the decision tree, a label value vector composed of label values of the items of the data, and a group information vector indicating grouping of the items of the data into the nodes; and calculating, using the numerical attribute value vector, the label value vector, and the group information vector, first to fourth frequencies, to evaluate the division condition using the first to fourth frequencies.

This application discloses a data processing method. The method includes: acquiring multiple first data subsets from multiple federated learning participants, wherein each federated learning participant provides one first data subset; processing the multiple first data subsets using a distance-based local differential privacy algorithm to obtain multiple second data subsets, wherein the distance is the distance between data within the first data subsets; combining the multiple second data subsets into a training data set, and inputting the training data set into a decision tree learning model for training to obtain a target decision tree.