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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.
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19 Jan 2018
TL;DR: In this article, a multi-dimensional parameter identification method and apparatus is presented, which comprises the steps of collecting a plurality of training samples to form a training sample set, wherein each training sample comprises multiple process parameters, each process parameter has corresponding attribute parameter and type, and multiple combinations of the attribute parameters and the types are provided.
Abstract: The invention discloses a multi-dimensional parameter identification method and apparatus. The identification method comprises the steps of collecting a plurality of training samples to form a training sample set, wherein each training sample comprises multiple process parameters, each process parameter has corresponding attribute parameter and type, and multiple combinations of the attribute parameters and the types are provided; according to the types in the training sample set, obtaining a distribution transmission information value of the training sample set; according to the distributiontransmission information value, obtaining an information gain of each process parameter; selecting the process parameter with the maximum information gain as a split node, and establishing a decisiontree; and according to the decision tree, performing type identification on new data. A decision tree model is built based on the information gains, so that the accuracy of the model is improved; thedata type is effectively and accurately identified; and a reliable basis is provided for intelligent parameter feedback.
TL;DR: A multi-attribute inventory classification model of maintenance parts and components has been put up based on the application of the decide tree model and fuzzy artificial neural network to improve maintenance service within equipment MRO enterprises.
Abstract: A multi-attribute inventory classification model of maintenance parts and components has been put up based on the application of the decide tree model and fuzzy artificial neural network to improve maintenance service within equipment MRO enterprises First the maintenance materials inventory styles are classified Then a decision tree model is defined based on inventory classification result The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and maintenance material inventory strategy can be decided with the classification tree and inventory strategy table In the end, the implementation of the model is presented
24 Jun 2011
TL;DR: A kind of classification scheme on extracting land cover information by the moderate resolution and high temporal resolution MODIS images, which can provide the technical support for rapid extraction of land cover Information and contribute to real-time dynimic monitoring is discussed.
Abstract: Extraction of Land Cover Information is important to make a study of global change. Taking East China as a study area, after MODIS data of this area in the four seasons are preprocessed, spectrum analysis of typical surface features are carried out. On these basis, by using decision tree classification, selecting spectral characteristics, NDVI and classification results of the maximum likelihood method as test variables, using proper thresholds for setting discriminating rules, a decision tree model is built. An assessment is given to the result by random samples. It is proved that the decision tree classification can get better accuracy result on the basis of the traditional supervised classification method. According to the method of Decision Tree, the image was classified in the consideration of multi-temporal and multi-spectral information. The research discusses a kind of classification scheme on extracting land cover information by the moderate resolution and high temporal resolution MODIS images, which can provide the technical support for rapid extraction of land cover information and contribute to real-time dynimic monitoring.
01 Jan 2019
TL;DR: In this chapter, teaching use of Wikipedia in all faculty members of the Universitat Oberta de Catalunya are used as data and an entropy-based decision tree algorithm was developed.
Abstract: Wikipedia is a source that has been used at many universities around the world for students to gain some skills and be motivated positively. In higher education, some academicians have a positive view on the teaching usefulness of Wikipedia, and some of them are determined to use classical teaching. In this chapter, teaching use of Wikipedia in all faculty members of the Universitat Oberta de Catalunya are used as data. Then an entropy-based decision tree algorithm was developed. Wikipedia users and non-users are classified according to some aspects with this decision tree. Thus, it can be understood that whether Wikipedia has been used as a teaching tool by academicians or not. So, researchers can have information about the usefulness of Wikipedia in teaching and the intentions in use of it by academicians.
29 Jun 2020
TL;DR: This paper studies decision tree models with various types of queries and finds thresholds functions often have non-trivial complexity in such models and can be used to provide interesting examples.
Abstract: In this paper we study decision tree models with various types of queries. For a given function it is usually not hard to determine the complexity in the standard decision tree model (each query evaluates a variable). However in more general settings showing tight lower bounds is substantially harder. Threshold functions often have non-trivial complexity in such models and can be used to provide interesting examples.