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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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Proceedings ArticleDOI
01 Jul 2017
TL;DR: Compared with the results of ITA, IITA is proved better to evaluate the risk of complex man-machine systems.
Abstract: The Incident Tree Analysis methodology (ITA) can consider the uncertain characteristics of accident by combining fuzzy logic and information flow approaches together. But the dependence of man-machine interaction is not considered in ITA approach, which ignores the differences of task scenarios meanwhile. In this paper, we put forward an Improved Incident Tree Analysis methodology (IITA) taking this dependence into account, and present its mathematic method. Firstly, information quantity and control sequence quantity are introduced and reformed to show the dependence of the man-machine interaction process at the information level. And then the steps of determining the two parameters values in different task scenarios are given. A vehicle-leaving-roadway accident is taken as an example to illustrate the proposed method. Contrasting with the results of ITA, IITA is proved better to evaluate the risk of complex man-machine systems.

1 citations

Journal ArticleDOI
TL;DR: In this paper , a decision tree predictive model was used for predicting live birth after surgery for moderate-to-severe intrauterine adhesions (IUAs) diagnosed via hysteroscopy.
Abstract: After treatment of intrauterine adhesions, the rate of re-adhesion is high and the pregnancy outcome unpredictable and unsatisfactory. This study established and verified a decision tree predictive model of live birth in patients after surgery for moderate-to-severe intrauterine adhesions (IUAs).A retrospective observational study initially comprised 394 patients with moderate-to-severe IUAs diagnosed via hysteroscopy. The patients underwent hysteroscopic adhesiolysis from January 2013 to January 2017, in a university-affiliated hospital. Follow-ups to determine the rate of live birth were conducted by telephone for at least the first postoperative year. A classification and regression tree algorithm was applied to establish a decision tree model of live birth after surgery.Within the final population of 374 patients, the total live birth rate after treatment was 29.7%. The accuracy of the model was 83.8%, and the area under the receiver operating characteristic curve (AUC) was 0.870 (95% CI 7.699-0.989). The root node variable was postoperative menstrual pattern. The predictive accuracy of the multivariate logistic regression model was 70.3%, and the AUC was 0.835 (95% CI 0.667-0.962).The decision tree predictive model is useful for predicting live birth after surgery for IUAs; postoperative menstrual pattern is a key factor in the model. This model will help clinicians make appropriate clinical decisions during patient consultations.

1 citations

Journal Article
TL;DR: This paper proposes a general method of extracting Web news from Chinese news websites by means of characteristic vector extraction and the decision tree learning algorithm.
Abstract: This paper proposes a general method of extracting Web news from Chinese news websitesBy means of characteristic vector extraction and the decision tree learning algorithm,the decision tree model of the textnode is established and sorted according to the website it comes from,and then a model base is set upWhen the url of a textnode is input,the website is searched according to the url the web page comes from,then the right model is selectedIf the proper model can not be found,the general one can be chosenThe experiments prove that this kind of method can attain a good result

1 citations

Patent
09 Aug 2019
TL;DR: In this paper, the authors proposed a safety tree based optimization method for the design and manufacturing of electric vehicles, which includes a plurality of bottom layer events, the middle layer events and the top layer events.
Abstract: The invention relates to an electric vehicle safety design optimization method based on a safety tree model, and the method comprises the steps: S1, building a safety tree which comprises a pluralityof bottom layer events, middle layer events, top layer events, and logic causal relationships and safety importance degrees among the bottom layer events, the middle layer events and the top layer events; S2, based on the security tree, performing security importance sorting on each underlying event; and S3, carrying out fault reconstruction analysis on the branches with high occurrence probability in the security tree based on the security importance of the underlying events, and reducing the occurrence probability of the underlying events in the branches based on an analysis result. Applyingthe method, potential safety hazards of certain typical problems and unreasonable design and production are discovered through mining and analyzing sample data fed back by different electric vehiclesin a manner of constructing and updating the security tree, and the design and manufacturing process of the electric vehicles is continuously perfected by reconstructing the problems.

1 citations

Journal ArticleDOI
TL;DR: Alternative implementations and propose two algorithms MB3-R and iMB3- R, which achieve better efficiency in terms of time and space, and a mathematical model for estimating the worst case complexity for induced subtree mining are developed.
Abstract: The increasing need for representing information through more complex structures where semantics and relationships among data objects can be more easily expressed has resulted in many semi-structured data sources. Structure comparison among semi-structured data objects can often reveal valuable information, and hence tree mining has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. We are primarily concerned with the task of mining frequent ordered induced and embedded subtrees from a database of rooted ordered labeled trees. Our previous contributions consist of the efficient Tree Model Guided (TMG) candidate enumeration approach for which we developed a mathematical model that provides an estimate of the worst case complexity for embedded subtree mining. This potentially reveals computationally impractical situations where one would be forced to constrain the mining process in some way so that at least some patterns can be discovered. This motivated our strategy of tackling the complexity of mining embedded subtrees by introducing the Level of Embedding constraint. Thus, when it is too costly to mine all frequent embedded subtrees, one can decrease the level of embedding constraint gradually down to 1, from which all the obtained frequent subtrees are induced subtrees. In this paper we develop alternative implementations and propose two algorithms MB3-R and iMB3-R, which achieve better efficiency in terms of time and space. Furthermore, we develop a mathematical model for estimating the worst case complexity for induced subtree mining. It is accompanied with a theoretical analysis of induced-embedded subtree relationships in terms of complexity for frequent subtree mining. Using synthetic and real world data we practically demonstrate the space and time efficiency of our new approach and provide some comparisons to the two well know algorithms for mining induced and embedded subtrees.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202224
2021101
2020163
2019158
2018121