About: Application of Decision Tree Algorithm Based on Clustering and Entropy Method Level Division for Regional Economic Index Selection

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Application of Decision Tree Algorithm Based on Clustering and Entropy Method Level Division for Regional Economic Index Selection
Creator	Zhang, Yi Yang, Gang
Source	PMC
abstract	The economy of a region is affected by many factors. The purpose of this study is to use the entropy method clustering and decision tree model fusion to find the main factors affecting the regional economy with the support of big data and empirical evidence. First extract some important indicators that affect the regional economy, and use the entropy method to find the relative weights and scores of these indicators. Then use K-means to divide these indicators into several intervals. Based on the entropy fusion model, obtain the ranking of each category of indicators, use these rankings as the objective value of the decision tree, and finally establish an economic indicator screening model. Participate in optimization and build a decision tree model that affects regional economic indicators. Through the visualization of the tree and the analysis of feature importance, you can intuitively see the main indicators that affect the regional economy, thereby achieving the research goals.
has issue date	2020-07-11(xsd:dateTime)
bibo:doi	10.1007/978-981-15-7205-0_5
has license	no-cc
sha1sum (hex)	e5c0d6e6dc0acf83d9f68603c8d63b66add4cb83
schema:url	https://doi.org/10.1007/978-981-15-7205-0_5
resource representing a document's title	Application of Decision Tree Algorithm Based on Clustering and Entropy Method Level Division for Regional Economic Index Selection
has PubMed Central identifier	PMC7351686
schema:publication	Data Mining and Big Data
resource representing a document's body	covid:e5c0d6e6dc0acf83d9f68603c8d63b66add4cb83#body_text
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