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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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28 Jul 2020
TL;DR: In this paper, a network real-time intrusion detection method based on a fast gradient boosting tree model was proposed, where the original traffic data captured in each time window is used as a data block, and statistical analysis on data blocks to generate a plurality of feature vectors; classifying the feature vectors by adopting a fastgradient boosting tree classification model, and distinguishing normal behaviors from network intrusion behaviors.
Abstract: The invention discloses a network real-time intrusion detection method based on a fast gradient boosting tree model. The method comprises the following steps: training a fast gradient boosting tree classification model by using training data; capturing network traffic data in continuous time windows, wherein the original traffic data captured in each time window is used as a data block; performingstatistical analysis on data blocks to generate a plurality of feature vectors; classifying the feature vectors by adopting a fast gradient boosting tree classification model, and distinguishing normal behaviors from network intrusion behaviors; and if the behavior is judged to be the network intrusion behavior, outputting a network intrusion alarm signal. The category deviation problem of classification performance can be solved, the false alarm rate can be reduced, and meanwhile, the matching process of the model establishment stage and the decision stage meets the real-time requirement.
24 Sep 2021
TL;DR: Wang et al. as discussed by the authors investigated the effectiveness of decision tree model for banks' decision making in granting personal credit loans, unlike the previous traditional bank credit analysis model, and found that decision tree can effectively process user information and analyze it, thus increasing prediction accuracy and reducing risk.
Abstract: With the rapid development and globalization of the economy, this article investigates the effectiveness of decision tree model for banks' decision making in granting personal credit loans, unlike the previous traditional bank credit analysis model. By collecting user data of lenders and analyzing it through decision tree model, this paper finds that decision tree model can effectively process user information and analyze it, thus increasing prediction accuracy and reducing risk, and finally helping banks to make decisions.
TL;DR: Wang et al. as discussed by the authors used the decision tree algorithm to study body classification rules, developed a decision tree body recognition model and judge the body shape of middle-aged women in Xinjiang, and the overall optimal decision tree is generated by means of hyperparameter pruning.
Abstract: Body type classification has a great influence on plate making and garment sizing system, and the accuracy of body type classification method will greatly affect the fit of garment production. The purpose of this paper is to use the decision tree algorithm to study body classification rules, develop a decision tree body recognition model and judge the body shape of middle-aged women in Xinjiang.,First, perform dimensionless processing on the collected data of 256 middle-aged women in Xinjiang, and the dimensionless data were used for K-means body clustering; Then, quantitatively analyze the effectiveness of different classification clusters based on the silhouette coefficients. Second, the decision tree algorithm is used to divide the classified sample data into a training set and a test set at a ratio of 70/30, and select the best node and the best branch based on the Gini coefficient to construct a classification tree. Last, the overall optimal decision tree is generated by means of hyperparameter pruning.,The body shape of middle-aged women in Xinjiang can be divided into three types: standard body, plump body and obese body. The decision tree model has an excellent effect on body classification of middle-aged women in Xinjiang (precision (macro), 95.46%; precision (micro), 95.95%; recall (macro), 95.46%; recall (micro), 95.95%; F1 (macro), 95.46%; F1 (micro), 95.95%).,For scientific research, this paper is conducive to increasing the regional body type theory and stimulating the establishment of a garment sizing subdivision system in Xinjiang. In terms of production practice, this paper not only establishes a model for judging the shape of middle-aged women in Xinjiang, but also provides reference data for intermediates of various sizes. In addition, to facilitate pattern-making and the establishment of a subdivision system for the size of middle-aged women's garments in Xinjiang, this paper provides the grading values of various body control parts of middle-aged women in Xinjiang.
TL;DR: In this paper , the authors used machine learning to classify air based on certain attributes and developed a prediction model based on time data to produce a predictive map of air pollution in Jakarta area for the next three years.
Abstract: Jakarta is a city in Indonesia that has a high population density that must pay attention to its health condition. Good air quality provides positive benefits to support public health so that they can be more productive at work and create fresh and healthy air. This study uses Machine Learning to classify air based on certain attributes. Then, the development of a prediction model based on time data is designed to produce a predictive map of air pollution in Jakarta area for the next 3 years. The methods applied are Decision Tree and Artificial Neural Networks. As a result, the Decision Tree and Artificial Neural Network models show very good accuracy for predictions from 2024 to 2026. The Decision Tree and Artificial Neural Network models get an accuracy of 98% and 94%. In 2025 the Decision Tree and Artificial Neural Network models get 99% and 93% accuracy. In 2026 the Decision Tree and Artificial Neural Network models get an accuracy of 94% and 93% which can be seen from the Decision Tree model which is superior to the Artificial Neural Network with a difference of 1 - 6%.
28 Oct 2022
TL;DR: In this paper , a data set of freshmen registration was collected and complete, and the machine learning algorithm of decision tree was used to predict the enrollment of freshmen in the University of Hong Kong.
Abstract: In this research, we collect and complete a data set of freshmen registration, and use the machine learning algorithm of decision tree to predict it. Our goal is to use this algorithm to verify whether the enrollment of freshmen can be predicted. Then we adopt the pruning scheme to optimize the decision tree. One is to prune the number of layers of the tree, and the other is to prune the number of samples on the leaf nodes. Then, we present the confusion matrix and F1 score to assess the impact of machine learning. The exploratory comes about to appear that the pruning scheme has greatly improved our prediction model.