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.

Object Distinction Based on Decision Tree

Abstract: In the problem of Object Distinction, different objects share identical names, retrieval one time will get many unrelated records and user cannot distinguish them easily. In this paper, we introduce a new method to distinguish objects with the same name, we first calculate the similarity values of the context attributes of the two objects with identical names, then we use these context attributes similarity values to build a decision tree model based on the training set. For the problem of object distinction for people, we combine the affiliation similarity with other context attributes similarity to judge whether the two people who share the same name correspond to the same people in real life. Experiments show that our method based on affiliation and Decision Tree can achieve high accuracy.

Effect of architectural tree model to the noise level of motor vehicle on demang lebar daun street palembang

Abstract: The research on the effect of architectural tree model to the noise level of motor vehicles on Demang Lebar Daun Street Palembang has been conducted. The purpose of this study was to analyze kind of architectural tree model can reduce the highest noise levels among the architectural tree models encountered and identify the architectural tree model. The method used was purposive sampling. Measurement of the noise level and architectural models were selected according to the type of the tree encountered on the left or right side of the street. The noise level measurement during the daylight was carried out simultaneously at a point of 1 meter in front of the tree, 1 and 5 meters behind the tree,for 10 minutes with the readings for every 5 seconds at 07:00, 10:00, 15:00 and 20:00. The results showed that The architectural model of Switenia magahoni tree was Rauh model, Lagerstroemia sp. tree was Troll model and Thyrsostachys siamensis tree was McClure model. The highest noise level reduction was from bamboo tree, respectively by 4,88 dB (A) and 8,52 dB (A) at the distances of 1 and 5 m. Keywords: noise level, reduction, architectural tree model

Design intention tree templatizing method

Abstract: The invention relates to a design thinking process model-oriented intention tree templatizing method, and belongs to research contents of knowledge resource management and application methods in the technical field of computer applications The method provides a solution for performing reuse-oriented knowledge processing in a design thinking process The method comprises the steps of firstly, performing design intention tree structure extraction based on a semantic relationship in a design thinking process model, and obtaining an intention tree model; and based on this, performing templatizing operation on a plurality of intention tree model instances Firstly, intention elements are renamed in a unified way to finish tagging of an intention tree; secondly, the tagged intention tree is taken as an input, and an intention tree template is constructed by identifying the semantic relationship among the intention elements; and finally structure recovery is performed on the intention tree template The method has a certain promotion effect for discovery and reuse methods of rich design thinking process knowledge, facilitates accumulation, sharing, integration and reuse of enterprise design process knowledge, and provides powerful support for improving enterprise innovation capability

Constructing Decision Trees for Unstructured Data

Abstract: The volume of unstructured data has been growing sharply as the era of Big Data arrives. Decision tree is one of the most widely used classification models designed for structured data. Unstructured data such as text need to be converted to structured format before being analyzed using decision tree model. In this paper, we discuss how to construct decision trees for datasets containing unstructured data. For that purpose, a decision tree construction algorithm called CUST was proposed, which can directly tackle unstructured data. CUST introduces the use of splitting criteria formed by unstructured attribute values, and reduces the number of scans on datasets by designing appropriate data structures. Experiments on real-world datasets show that CUST improves the efficiency of building classifiers for unstructured data and performs as well as, if not better than existing solutions in classification accuracy.