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.

Decision Tree and Its Key Techniques

Abstract: Decision tree is an important method in induction learning as well as in data mining,which can be used to form classification and predictive model.Introduces decision tree and points out its key techniques: the choice of testing feature and tree pruning.It summarizes the main features of every algorithm by analyzing and comparing a variety of typical classifiers to provide a basis for selecting or improving the algorithms in data mining.Finally,through an instance,this paper shows the application of decision tree in production.

Means for and method of displaying a visual decision tree model

Abstract: A computer implemented means for and method of displaying a visual decision tree model in a symbol-based table is disclosed This visual model includes a plurality of visual objects each of the visual objects being linked to at least one other object to form a decision tree The invention is characterised in that after the initial object the tree displays only visual objects that depend from objects which have been selected by a user Thus the device displays only the path selected

Tree-Structure based Hybrid Computational Intelligence: Theoretical Foundations and Applications

Abstract: Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable

Malicious program dynamic identification method based on decision-making tree model

Abstract: The invention discloses a malicious program dynamic identification method based on a decision-making tree model. The method comprise the steps of 1, establishing a behavior collection sandbox module; 2, collecting black and white samples to form a training sample set; 3, capturing all behaviors generated when samples are operated; 4, calculating and combining feature identification nodes and behavior vectors of various behaviors of white samples to obtain a white vector set; 5, calculating and combining the feature identification nodes and the behavior vectors of the various behaviors of black samples to form a black vector set; 6, training the white vector set, the black vector set and a machine learning model to generate a decision-making tree; 7, capturing complete behaviors based on unknown sample programs according to a sandbox; 8, calculating behavior feature identification of the unknown samples; 9, calculating the behavior vectors of the samples and inputting the behavior vectors into the decision-making tree for identification; and 10, outputting sample identification results by the decision-making tree. Through adoption of the steps, a malicious program dynamic identification purpose is achieved through utilization of the decision-making tree model, and the problem that the efficiency is low and a process is complex in a malicious sample analysis process is solved.