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.

Application of Data Mining for Decision Tree Model of Multi-variety Discrete Production and Manufacture

Abstract: In this paper, taking the decision attributes, including order number, product number, manhour and comprehensive evaluation into account, the decision tree model of discrete production and manufacture has been presented. And C4.5 algorithm has been used to construct the decision tree recursively in a topdown manner, in which the topmost node is the root node, each internal node denotes an attribute test, each branch represents an outcome of the test, and each leaf node represents classes. Then RFC (Remote Function Call) has been used to extract data from SAP (Systems Applications and Products in Data Processing) R/3 system to decision-making database, and DTS (Data Transformation Service) used to extract data from MES (Manufacturing Execution System) to decision-making database, which can supply useful data resource for data mining. Finally, a case study has been analyzed to show the application of the decision tree model. The decision tree has been implied to be references to the real discrete production and manufacture.

A new quadtree complexity theorem

Abstract: Complexity of hierarchical representation of images is defined as the total number of nodes in the representation tree. An a priori knowledge of this quantity is of considerable interest in problems involving tree search, storage and transmission of imagery. This paper proposes a systematic approach for the evaluation of quadtree complexity that is based on a flexible linkage paradigm. It is further realized that a quadtree may undergo a complexity reduction through node condensation. This event is fully modeled and absorbed in the expected complexity expression through a multidimensional weighting function. Inspection of the weighting surface provides a more clear view of the interaction of quadtree complexity and the random image model. >

Techniques for interactive decision trees

Abstract: Techniques for providing interactive decision trees are included. For example, a system is provided that stores data related to a decision tree, wherein the data includes one or more data structures and one or more portions of code. The system receives input corresponding to an interaction request associated with a modification to the decision tree. The system determines whether the modification requires multiple-processing iterations of the distributed data set. The system generates an application layer modified decision tree when the generating requires no multiple-processing iterations of the distributed data set. The system facilitates server layer modification of the decision tree when the modification requires multiple-processing iterations of the distributed data set. The system generates a representation of the application layer modified decision tree or the server layer modified decision tree.

A Research on the Classification Decision Tree Model based on Network Behavior

Abstract: Higher vocational students’ individualized learning behavior is an important higher education research direction. In this paper, a new model was proposed on the classification decision tree. First, we gather vocational students' psychology, behavior, methods and effect data in the process of online learning. After screening, quantification, classification and evaluation, learners were classified according to internal factors and individuality characteristics. This paper is supported by Zhejiang High-level skills training and technology innovation projects(2011R30056).