8 results about "Decision tree learning" patented technology

The invention relates to a wind driven generator blade icing fault prediction method based on deep learning, and the method comprises the following steps: constructing an integrated learning classifier XGBoost which is a gradient lifting integrated algorithm based on a decision tree, and gives consideration to a linear model solver and a decision tree learning algorithm; deep feature learning: through the ability of deep feature learning, mining the internal relation between the operation parameters and the blade health state, establishing a multi-layer deep network structure, deeply learning the nonlinear high-order correlation between the operation parameters of the wind driven generator and an output target, and completing the complex mapping from each working condition parameter to hidden layer feature output; and carrying out classification mode identification of an integrated classifier, taking hidden layer output information of deep network learning under different dimensions as input of an integrated learning classifier XGBoost, obtaining evaluation indexes under different feature dimensions through weighted classification accuracy of a plurality of base classifiers, and carrying out prediction on the icing fault of the wind driven generator blade.

A secret decision tree learning device in one embodiment is a secret decision tree learning device that learns a decision tree through secret calculation, and comprises: an input unit that inputs a data set consisting of multiple records, wherein the multiple records include attribute values of one or more explanatory variables and attribute values of a target variable; and a learning unit that divides the data set into all nodes contained in each level of the decision tree, thereby learning the decision tree.

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 present application relates to the field of remote sensing monitoring, in particular to a mangrove inter-specific classification method and device based on unmanned aerial vehicle remote sensing, adopting a decision tree learning algorithm to update feature threshold values in a feature corresponding relationship of a plurality of mangrove plant categories in an initial multi-modal plant knowledge graph, obtaining an updated multi-modal plant knowledge graph, performing inter-specific classification reasoning according to a plurality of types of feature data in the extracted feature data set of the region to be classified, feature values corresponding to the feature data, and the updated multi-modal plant knowledge graph, obtaining a mangrove inter-specific classification result, which integrates classification knowledge, overcomes the shortcomings of a method that simply relies on data, and improves the accuracy, efficiency and reliability of mangrove inter-specific classification.

The present invention relates to the field of artificial intelligence technology, and in particular to an automatic generation method based on a handbag design scheme. The method comprises the following steps: constructing a three-dimensional structure model for design history data to obtain a three-dimensional structure model; performing time series change migration to obtain a variable force structure dynamic model; performing multimodal learning of thermal gradients on the three-dimensional structure model for handbag temperature test data to obtain a thermal state model set; performing brightness level contrast evaluation on handbag shooting image data and handbag design image data to obtain contrast data; performing printing material adaptability analysis on handbag printing material data to obtain printing material adaptability data; performing design decision tree learning migration on the thermal state model set to obtain a design decision tree model; performing heat loss decision pruning processing on the design decision tree model to obtain a handbag automatic design model. The present invention achieves an intelligent and automatic generation effect based on a handbag design scheme.

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.