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.

Three-Dimensional Modeling of Trees Group with Spatial Characteristics and Its Space Subdivision Method

Abstract: Modeling of trees group is one of the key issues of building virtual forest environment.A new model of trees group with spatial characteristics is proposed.The model focusing on describing the spatial structure of tree populations and their topology relations,takes into account the overall distribution of trees group.In the process of modeling,TIN is used to interconnect roots of adjacent trees so as to form Delaunay triangulation networks.Rule-based method is used to establish single tree model with data from the sample plots.Compact octree is used to divide up the model so as to effectively simulate and analyze trees environment in three dimensions.Finally the tree model and modeling method are realized by Visual C++ and OpenGL.The modeling method is tested by measurement data.

Method and system for creating a parameter of quality forecast for a forecasting model performed in a machine learning algorithm

Abstract: FIELD: computer equipment.SUBSTANCE: invention relates to the field of computer equipment. Method for determining the prediction quality parameter for a decision tree in a prognostic decision tree model is disclosed, this level of the decision tree has at least one node; a forecast quality parameter is used to assess the forecast quality of the predictive model of the decision tree at this iteration of learning the decision tree, method is performed by a machine learning system that performs a predictive model of the decision tree, the method includes: obtaining access from a permanent machine-readable carrier of a machine learning system, set of learning objects, each learning object from a set of learning objects includes an indication of a document and purpose associated with the document; organizing a set of learning objects into an ordered list of learning objects, the ordered list of learning objects being organized in such a way, that for each learning object in the ordered list of learning objects there is at least one of: (i) the previous learning object that is before the given learning object, and (ii) a subsequent training object, which is located after the given training object; descent of a set of learning objects on the decision tree in such a way that each of the set of learning objects is classified by the model of the decision tree at this iteration of learning to a given child node from at least one node at a given level of the decision tree; creating a forecast quality parameter for the decision tree by creating for this training object that was classified into this child node, forecast quality parameter, the creation is performed based on the goals of only those learning objects that are before the learning object in the ordered list of learning objects.EFFECT: technical result is the determination of the forecast quality parameter for a decision tree in a predictive model of the decision tree.30 cl, 13 dwg

Trustable Decision Tree Model using Else-Tree Classifier

Abstract: Abstract With advances in machine learning and artificial intelligence, learning models have been used in many decision-making and classification applications. The nature of critical applications, which require a high level of trust in the prediction results, has motivated researchers to study classification algorithms that would minimize misclassification errors. In our study, we have developed the {\em trustable machine learning methodology} that allows the classification model to learn its limitations by rejecting the decision on cases likely yield to misclassificationsand hence produce highly confident outputs. This paper presents our trustable decision tree model through the development of the {\em Else-Tree} classifier algorithm. In contrast to the traditional decision tree models, which use a measurement of impurity to build the tree and decide class labels based on the majority of data samples at the leaf nodes, Else-Tree analyzes homogeneous regions of training data with similar attribute values and the same class label. After identifying the longest or most populated contiguous range per class, a decision node is created for that class, and the rest of the ranges are fed into the else branch to continue building the tree model. The Else-Tree model does not necessarily assign a class for conflicting or doubtful samples. Instead, it has an else-leaf node, led by the last else branch, to determine rejected or undecided data. The Else-Tree classifier has been evaluated and compared with other models through multiple datasets. The results show that Else-Tree can minimize the rate of misclassification.

Complexity of ternary decision diagrams for representation of discrete functions

Abstract: We discuss the complexity of different bit-level and word-level ternary decision diagrams (TDDs) for the representation of discrete functions of two-valued variables, including switching functions as a particular example.

Classification Prediction of the Foot Disease Pattern Using Decision Tree Model

Abstract: Datamining is used to find out desired important and meaningful knowledge in large scale data. The decision tree in classification algorithms has been applied to categorical attributes and numeric attributes in different domains. The purpose of study was to acquire significant information between singular disease groups and biomechanical parameters related with symptoms by developing prediction model. Sample data of 90 patient’s records diagnosed with a singular disease was selected for analysis, in total 2418 data. A dependent variable was composed of 9 singular disease groups. 18 of 32 independent variables closely related to disease were selected and optimized. After object data was divided into training data and test data, C5.0 algorithm was applied for analysis. In conclusion, 10 diagnosis rules were created and major symptom information was verified. On the basis of the study, additional analysis with utilizing other datamining methods will be performed to improve accuracy from now on.