Topic
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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12 Nov 2018
TL;DR: The system created are used to predict the length of study period required for students to complete their studies based on their grades and have online consultation features that students use with their academic lecturer for academic consultations.
Abstract: The system created are used to predict the length of study period required for students to complete their studies based on their grades. The system created also have online consultation features that students use with their academic lecturer for academic consultations. To find the model tree with good accuracy, the system will use k-fold cross validation in the process of making model tree. Testing prediction system using students data from 2008 to 2012 who have completed their studies. The value data used is all mandatory courses in the Faculty of Information Technology except for thesis courses. Based on the tests performed, the system can already run and used in accordance with the design made. The test is to compare the accuracy of the selected tree model from different k values in the k-fold cross validation process. The results obtained show that if the value of k the greater, then the accuracy obtained better.
3 citations
01 Dec 2018
TL;DR: A discretization method for floating-point numbers in decision tree model is presented by converting the floating- point numbers into integers to reduce the storage and computing resources required by the hardware implementation of decision tree, without affecting the classification performance of the classifier.
Abstract: Decision tree is one of the most popular supervised machine learning algorithms. Due to the rapid increase in the amount of data, many application scenarios require higher classification speed. Therefore, a variety of decision tree classification acceleration algorithms based on FPGA are proposed. These methods focus on improving the classification speed by designing effective pipeline architecture. However, the impact of floating-point numbers on storage and computing resources in the hardware implementation of the decision tree is ignored. In this paper, we present a discretization method for floating-point numbers in decision tree model by converting the floating-point numbers into integers. This method reduces the storage and computing resources required by the hardware implementation of decision tree, without affecting the classification performance of the classifier.
3 citations
17 Sep 2020
TL;DR: In this article, an improved XGBoost model based on Spark is proposed to detect credit card fraud and the experimental results show that the proposed model can accurately and efficiently predict credit card Fraud and has a good practical effect.
Abstract: Credit card fraud causes huge economic losses for many financial institutions. Given the imbalance of dataset and the huge amount of data in the field of credit card fraud, an improved XGBoost model based on Spark is proposed. In this project, the Smote algorithm was used to to balance the training set. And the XGBoost classifier based on Spark was used as the fraud detection mechanism. Finally, the test sets were classified in parallel. In the model comparison experiment, the model proposed in this project is compared with logistic regression model, decision tree model, random forest model, and original XGBoost model. The experimental results show that in the three metrics of Recall, Fl-Score, and AUC, the model proposed in this project is the best, which is 9.1%, 1.4%, and 1.2% ahead of the model ranked second respectively. In the speedup experiment, the speedup on the dataset of 70,000, 140,000, and 280,000 samples are 2.06, 3.28, and 3.75 respectively. The experimental results of these two parts show that the proposed model can accurately and efficiently predict credit card fraud and has a good practical effect.
3 citations
3 citations
03 Apr 1995
TL;DR: It is proved that, for any partition of the input variables and for any moduls k and m, GAP and MODk-GAP have MOD m -communication complexity Ω(n), where n denotes the number of nodes of the graphs under consideration.
Abstract: We investigate the modular communication complexity of the graph accessibility problem GAP and its modular counting versions MODk-GAP, k≥2 Due to arguments concerning variation ranks and certain projection reductions, we prove that, for any partition of the input variables and for any moduls k and m, GAP and MODk-GAP have MOD m -communication complexity Ω(n), where n denotes the number of nodes of the graphs under consideration
3 citations