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.

Analysis on Influencing Factors of Dance Teaching Effect in Colleges Based on Data Analysis and Decision Tree Model

Abstract: In colleges, dance teaching is influenced by a variety of factors. It is very difficult to clarify how much each factor impacts the teaching effect. To overcome the difficulty, this paper explores the factors affecting the dance teaching effect in colleges based on data analysis and decision tree model. Firstly, the authors enumerated the goals of dance teaching for college students in the new era, and then summed up the constraints on the influencing factors of dance teaching effect in colleges. On this basis, an analysis model was established for the influencing factors, while the corresponding extensible decision tree was set up and verified through example analysis. The research findings shed new light on the theories of dance teaching in colleges, and provide an analysis model with great application potential.

Real-time hand postures recognition using low computational complexity Artificial Neural Networks and Support Vector Machines

Abstract: This paper proposes two main techniques for reduce computational complexity on artificial neural networks, using piecewise linear activation function, and support vector machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.

Study on Constructing Generalized Decision Tree by Using DNA Coding Genetic Algorithm

Abstract: Decision tree is mainly used in classification and predictive model. The introduction of Generalized Decision Tree (GDT) realized the unification of classification rules and decision tree structure. Meanwhile, a new method that based on DNA coding genetic algorithm to construct decision tree was proposed. It firstly classified dataset by C4.5 to get initial rule sets, then optimized the rule sets by using the algorithm to construct decision tree. It is proved that this method can avoid the weakness of traditional decision tree constructing process and has good parallelism.

Unimprovable upper bounds on time complexity of decision trees

Abstract: The paper is devoted to investigation of behavior of global Shannon functions which produce unimprovable upper bounds on time complexity of decision trees over arbitrary information systems.