33 results about "ID3 algorithm" patented technology

The invention discloses a sea-surface oil spill monitoring threshold setting method based on an ID3 algorithm and a neural network and mainly solves the problem that the prior art cannot well apply tocomplex monitoring environments and changing detection distance. The method includes: using a monitoring system to acquire the environment information such as weather, tide, sun altitude and ultraviolet of a to-be-monitored sea area through the Internet; selecting and using an ID3 decision tree monitoring threshold or a neural network monitoring threshold according to a set time limit; performingmatched filtering on the monitoring data of the to-be-monitored sea surface to acquire a data maximum value, judging whether the maximum value exceeds the monitoring threshold or not, if so, transmitting an alarm, and if not, judging that oil spill does not occur on the sea surface; after staff receive the alarm, by the staff, judging whether oil spill actually occurs or not through a real-time image, if so, processing the oil spill immediately, and if not, manually modifying the threshold, and reconstructing an ID3 decision tree or training the neural network. By the method applicable to sea-surface oil spill monitoring, monitoring precision is increased.

The invention provides a method for predicting the hazard level of rock burst based on a local weighted C4.5 algorithm, and relates to the technical field of rock burst prediction. This method first uses the MDLP method to discretize the continuous attribute data in the sample data, then uses the local weighting method to select the training set and calculate the sample weight, uses the sample weight to calculate the information gain rate of each attribute, and selects the sample attribute according to the information gain rate as C4.5 The root node of the decision tree and the split attributes of other branch nodes. Finally, the decision tree is pessimistically pruned by using the sample weight instead of the number of samples to realize the prediction of the rock burst hazard level in the predicted area. The method for predicting the hazard level of rock burst based on the local weighted C4.5 algorithm provided by the present invention overcomes the disadvantage of using information gain to select node split attributes in the ID3 algorithm, and avoids the problem of over-fitting. The prediction accuracy of the model is high.

The invention provides a method for predicting and evaluating the strength degradation of concrete in a marine environment. Through experiments, the data of the strength degradation of concrete with age in a marine environment under different proportions is detected, and the obtained operating data are divided into a training group and a test group. The factors that affect the concrete strength are taken as the factor attribute set, and the state of strength degradation is taken as the deterioration result attribute set, and the decision tree modeling is carried out, and the output branch is selected according to the attribute value of the corresponding attribute until it reaches the leaf node, and the operation category stored in the leaf node is As the output of the analysis results, the performance of the model is evaluated and the established decision tree is pruned using the C4.5 algorithm. Aiming at the degradation conditions of complex concrete service environment and the final degradation state response, the present invention uses the ID3 algorithm to obtain the final decision tree diagram, and uses the C4.5 optimization algorithm to reduce the branches of the decision tree, which can significantly improve the performance of the decision tree and is better Prediction and evaluation of the concrete strength degradation state under marine service conditions can be made, so that real-time information on the degradation of building strength can be obtained, and the danger caused by the durability damage of buildings can be evaluated in advance.

The present invention relates to the field of artificial intelligence, and provides a course recommendation method, device, equipment and medium based on root cause analysis, which can identify the label of each course in course data, and construct training samples for training prediction according to the label of each course and course data Model, according to the prediction model, the improved ID3 algorithm is used to calculate the information entropy of each tag, the information gain of each tag is calculated according to the information entropy of each tag, and the course recommendation list is generated according to the information gain of each tag, combined with the ID3 algorithm The root cause analysis of the impact of training courses on performance ensures the interpretability and accuracy of the analysis results, assists in the analysis of training courses that have a greater impact on performance, and then combines artificial intelligence to automatically recommend courses for continuous tracking , Improve performance and retention, so that training can truly help the team's improvement and personal development. In addition, the present invention also relates to block chain technology, and the prediction model can be stored in block chain nodes.

The invention discloses a method and system for classifying data by adopting a decision tree. The method comprises the following steps of: parallel computing the information gain of each attribute in training data based on a MapReduce mechanism, and selecting optimum division decision attributes as nodes to construct the decision tree; based on the decision tree, classifying input data records. Aparallel decision tree ID3 algorithm is realized based on the MapReduce. A large data set can be processed, and the parallel efficiency is high. The parallel computing is realized for the nodes in the decision tree and the nodes in the same layer.

The invention discloses a road condition information prediction method based on an improved decision tree algorithm, comprising: determining and analyzing the attributes of the road connectivity based on the influencing factors of the road connectivity; collecting road data, preprocessing the data, calculating information entropy, and calculating various attributes The attribute entropy of each attribute is calculated based on the correlation function value of each attribute, and the weight value of each attribute is calculated based on the correlation function value of each attribute; the information gain of each attribute is calculated based on information entropy, attribute entropy of each attribute and weight value of each attribute , according to the magnitude of the information gain of each attribute, the decision tree is constructed, and the road conditions are predicted according to the decision tree. The present invention builds a decision tree by calculating the correlation function value of the attribute and the attribute weight value obtained by information entropy expansion operation, which can overcome the problem that the traditional ID3 algorithm tends to select elements with more possible values ​​as high-weight attributes. Use the constructed decision tree to predict the degree of road congestion improvement in the next period.