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.

Method and device for sorting search results based on decision-making trees

Abstract: The invention relates to the technical field of data searching, and discloses a method and a device for sorting search results based on decision-making trees. The method in the invention comprises the following steps of: obtaining a training set required for establishing at least one decision-making tree for sorting; dividing a calculation system of the decision-making tree into N feature process sets respectively corresponding to N training features; calculating the optimal split nodes of various decision-making trees and the optimal split values corresponding to the optimal split nodes through the feature process sets, and establishing various decision-making trees according to the optimal split nodes and the optimal split values; and sorting the search results based on all the decision-making trees. According to the method disclosed by the invention, the time consumed by calculation can be greatly reduced when the training data volume in a sorting training set is very high, such as hundreds of millions; and particularly, a high-quality decision-making tree model for sorting can be trained rapidly and accurately when the data volume of a database corresponding to a search engine is huge.

SMT: Sparse multivariate tree

Abstract: A multivariate decision tree attempts to improve upon the single variable split in a traditional tree. With the increase in datasets with many features and a small number of labeled instances in a variety of domains bioinformatics, text mining, etc., a traditional tree-based approach with a greedy variable selection at a node may omit important information. Therefore, the recursive partitioning idea of a simple decision tree combined with the intrinsic feature selection of L1 regularized logistic regression LR at each node is a natural choice for a multivariate tree model that is simple, but broadly applicable. This natural solution leads to the sparse multivariate tree SMT considered here. SMT can naturally handle non-time-series data and is extended to handle time-series classification problems with the power of extracting interpretable temporal patterns e.g., means, slopes, and deviations. Binary L1 regularized LR models are used here for binary classification problems. However, SMT may be extended to solve multiclass problems with multinomial LR models. The accuracy and computational efficiency of SMT is compared to a large number of competitors on time series and non-time-series data. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013

The Improvement and Application of Structure Optimization Algorithm Based on Flexible Neural Trees

Abstract: in this paper, the improved structural optimization Algorithm of the flexible neural tree model is employed to select the parameters for the industrial production. With the highest accuracy and the shortest time to find the important parameters affecting the production so as to provide a theoretical support for the control of fluid industry production. In the period of learning of the flexible neural tree model, the evolution generation of Algorithm is not a fixed value and the mean error rate is utilized to control the evolution generation. The flexible neural tree model’s structure and parameters are optimized by the probabilistic incremental program evolution and the simulation annealing, respectively. The process of the decomposing furnace, which is one of the most important processes of the Cement productions, is the object of this article. And it has been demonstrated that the given method is very effective.

Some Results in Computational Complexity

Abstract: Some Results in Computational Complexity Ali Juma Master of Science Graduate Department of Computer Science University of Toronto 2005 In this thesis, we present some results in computational complexity. We consider two approaches for showing that #P has polynomial-size circuits. These approaches use ideas from the interactive proof for #3-SAT. We show that these approaches fail. We discuss whether there are instance checkers for languages complete for the class of approximate counting problems. We provide evidence that such instance checkers do not exist. We discuss the extent to which proofs of hierarchy theorems are constructive. We examine the problems that arise when trying to make the proof of Fortnow and Santhanam’s nonuniform BPP hierarchy theorem more constructive.

Network pseudo public opinion identification method based on combined optimization decision tree

Abstract: The invention provides a network pseudo public opinion identification method based on a combined optimization decision tree. The method comprises the following steps that S1, a public opinion attribute set of a network public opinion event is obtained, wherein the public opinion attribute set comprises one or more of the variety and number of first publish media and forwarding media, the total posting number, the number of posting within 48 hours, the number of posting within a week, duration, the number of viewpoints, the largest opinion ratio and the forwarding number; S2, public opinion attribute values corresponding to at least one public opinion attribute in the public opinion attribute set are input into a decision tree model, and an identification result of the network public opinion event is obtained. According to the method, the public opinion attribute values corresponding to at least one public opinion attribute in the public opinion attribute set are input into the decisiontree model, and network pseudo public opinions are more accurately and rapidly identified through the decision tree model.