Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

An Application of Heuristic-Search Based Tree Model in Fault Analysis of Distribution Network

Abstract: Along with the increase of need for high reliability of the power system security, it is important to build up a distribution fault analysis network and processing system, which can provide decision-making help for dispatching personnel. This paper introduced framework of the distribution network fault analysis and processing system, and analyze the fault location, isolation, generation and evaluation of power supply reconfiguration strategy. Tree model and Heuristic search were used for fault location and power supply route switching strategy.

An algorithm of decision tree construction based on attribute support degree

Abstract: Decision tree algorithms are widely used in data mining and classification systems, because of theirs faster speed, higher accuracy and easier structures. The key to constructing a good decision tree lies in the reasonable choice of attributes. Based on rough set theory and granular computing theory, the paper proposes a concept of attribute support degree to select attributes, using the concept a novel decision tree construction algorithm is presented. The results of experiments on the UCI dataset show that, the decision tree constructed by the new approach tend to have better classification accuracy and stability than ID3 and C4.5.

bi-objective multi-agent flexible neural tree model

Abstract: time, Multi-Agent architecture of feed forward Flexible Neural Tree model (MAFNT) is introduced for parallelizing the NN training. Moreover, looking for the best topology of NN, for a given problem, accounts for the large feasible solutions provided. Agents manage different NN structures simultaneously for optimization using Evolutionary Computation algorithms. However, different agents need communications to produce cooperative work and to reach the near-optimum solution. For that, a negotiation process is designed for the multi-agent system. It distributes tasks and organizes the message traffic between agents. They followed negotiation strategy to ensure interactions between themselves, overcoming the difference of NN structures. This model was evaluated through real problem classification datasets. Compared to some existing classifiers, MAFNT shows better performance respecting NN

Data processing method, device and system for machine learning model

Abstract: A data processing method, device and system for a machine learning model, that can transmit data of a data holder to a model holder by adopting an oblivious transfer protocol or a homomorphic encryption algorithm (102), and call a function for realizing comparison functionality by each level of a tree model (104), while ignoring a path choice of the data in the tree model. The oblivious transfer protocol or the homomorphic encryption algorithm is combined with the function for realizing comparison functionality, so as to guarantee that the model holder and the data holder do not reveal any information to each other, except a model output result, thereby ensuring the security and privacy of the data interaction.

Account analysis method, device and storage medium

Abstract: The invention discloses an account analysis method, device and storage medium, and belongs to the field of the machine learning. The method comprises the following steps: acquiring a financial producttransaction record of a to-be-analyzed account in the latest preset duration; determining account data of the to-be-analyzed account; inputting the account data into a preset decision tree model, wherein the preset decision tree model is the model obtained through the training of a preset training set, the preset training set comprises a risk account and a non-risk account and corresponding account data, and the risk account is the account for performing interest arbitrage through an illegal way; and outputting through the preset decision tree model to obtain the risk condition of the to-be-analyzed account. The preset decision tree model is obtained by training the decision tree model in advance, and the preset decision tree model is used for predicting whether the to-be-analyzed accountis the risk account, the analysis efficiency on the to-be-analyzed account is improved, the to-be-analyze account can be predicted as the risk account before the to-be-analyzed account causes a lossto the financial company, and the timeliness for identifying the risk user is improved.