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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 Dec 2017
TL;DR: A diagnostic method for breast cancer based on the decision tree model combined with feature selection, which significantly outperforms the state-of-theart method with respect to a variety of metrics.
Abstract: Breast cancer is the second leading cause of cancer death in women. At the same time, it is one of the most curable cancer if it could be diagnosed early. More and more researchers have confirmed that the decision tree model has a good ability to accurately diagnose. This paper presents a diagnostic method for breast cancer based on the decision tree model combined with feature selection. Experiments were conducted on different training test divisions of the Wisconsin Breast Cancer Data Set (WBCD), a common method used by researchers to diagnose breast cancer based on machine learning methods. In this paper, in order to reduce the complexity of the decision tree model, this paper proposed to delete some highly relevant features of ... After data correlation and independence tests, it finally chosed the tumor thickness, cell shape consistency, single epithelial cell size and mitosis as a subset of the decision tree model. Experimental results show that the classification accuracy (94.3%) significantly outperforms the state-of-theart method with respect to a variety of metrics.

9 citations

Journal ArticleDOI
TL;DR: A new general image segmentation system is presented, based on the calculation of a tree representation of the original image in which image regions are assigned to tree nodes, followed by a correspondence process with a model tree, which embeds the a priori knowledge about the images.

9 citations

Book ChapterDOI
01 Jul 2007
TL;DR: Compared to the automatic modeling process as typically applied in current decision tree modeling tools, IVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users' understanding of the algorithm, and give them greater satisfaction with the task.
Abstract: Data mining (DM) modeling is a process of transforming information enfolded in a dataset into a form amenable to human cognition. Most current DM tools only support automatic modeling, during which uses have little interaction with computing machines other than assigning some parameter values at the beginning of the process. Arbitrary selection of parameter values, however, can lead to an unproductive modeling process. Automatic modeling also downplays the key roles played by humans in current knowledge discovery systems. Classification is the process of finding models that distinguish data classes in order to predict the class of objects whose class labels are unknown. Decision tree is one of the most widely used classification tools. A novel interactive visual decision tree (IVDT) classification process has been proposed in this research; it aims to facilitate decision tree classification process regarding enhancing users' understanding and improving the effectiveness of the process by combining the flexibility, creativity, and general knowledge of humans with the enormous storage capacity and computational power of computers. An IVDT for categorical input attributes has been developed and experimented on twenty subjects to test three hypotheses regarding its potential advantages. The experimental results suggested that compared to the automatic modeling process as typically applied in current decision tree modeling tools, IVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users' understanding of the algorithm, and give them greater satisfaction with the task.

9 citations

Proceedings ArticleDOI
11 Oct 2009
TL;DR: Of the four models the Bayesian network model performed best while the Survival analysis did worst, and an integrated contrast among theFour models from the applicability of model in theory and experimental comparison has been processed.
Abstract: Decision tree, neural network and logistic regression were applied frequently as models of customer churn prediction, but the application of them has been mature and they are difficult to be improved. In this paper, Bayesian Networks, Support Vector Machines, Rough Sets and Survival Analysis were selected for experimental comparison study. An integrated contrast among the four models from the applicability of model in theory and experimental comparison has been processed. Overall, of the four models the Bayesian network model performed best while the Survival analysis did worst.

9 citations

Proceedings Article
01 Nov 2010
TL;DR: A popular decision tree in DSM, which is known as Hoeffding Tree vis-à-vis that of C4.5, is evaluated for investigating the apparent differences between the decision trees.
Abstract: Data Stream mining (DSM) is claimed to be the successor of traditional data mining where it is capable of mining continuous incoming data streams in real-time with an acceptable performance Nowadays many computer applications evolved to online and on-demand basis, fresh data are feeding in at high speeds Not only a decision response needs to be made rapidly, the trained decision tree models would have to be updated recurrently as frequent as the latest data arrive By the nature of traditional data mining, training datasets are assumed structured and static, and the decision tree models are either refreshed in batches or never That is, the full dataset will be completely scanned (sometimes in multiple repetitions), induction of rules by Greedy algorithm that proceeds in manner of divide-and-conquer in the case of constructing a C45 decision tree DSM on the other hand progressively builds and renews the decision tree model at a time when a new pass of data come by In this paper, we evaluated the performance of a popular decision tree in DSM, which is known as Hoeffding Tree vis-a-vis that of C45 A good mix of types of datasets was used in the experiments for investigating the apparent differences between the decision trees An open-source DSM simulator was programmed in JAVA for the experiments

9 citations


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