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Selecting decision trees for power system security assessment

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Energy and AI 2021年第4期6卷 21-30页

作者： Al-Amin B.Bugaje Jochen L.Cremer Mingyang Sun Goran Strbac Department of Electrical&Electronic Engineering Imperial College LondonLondonSW72AZUK Department of Electrical Sustainable Energy TU DelftMekelweg 52628 CD DelftNetherlands Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

Power systems transport an increasing amount of electricity,and in the future,involve more distributed renewables and dynamic interactions of the *** system response to disturbances must be secure and predictable to avoid power *** system response can be simulated in the time ***,this dynamic security assessment(DSA)is not computationally tractable in *** promising is to train decision trees(DTs)from machine learning as interpretable classifiers to predict whether the systemwide responses to disturbances are *** most research,selecting the best DT model focuses on predictive ***,it is insufficient to focus solely on predictive *** alarms and false alarms have drastically different costs,and as security assessment is a critical task,interpretability is crucial for *** this work,the multiple objectives of interpretability,varying costs,and accuracies are considered for DT model *** propose a rigorous workflow to select the best *** addition,we present two graphical approaches for visual inspection to illustrate the selection sensitivity to probability and impacts of *** propose cost curves to inspect selection combining all three objectives for the first *** studies on the IEEE 68 bus system and the French system show that the proposed approach allows for better DT-selections,with an 80%increase in interpretability,5%reduction in expected operating cost,while making almost zero accuracy *** proposed approach scales well with larger systems and can be used for models beyond ***,this work provides insights into criteria for model selection in a promising application for methods from artificial intelligence(AI).

关键词： Dynamic security assessment Machine learning decision trees ROC curve Cost curves Cost sensitivity

Analysis of NIR spectroscopic data using decision trees and their ensembles

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Journal of Analysis and Testing 2018年第3期2卷 274-289页

作者： Sergey Kucheryavskiy Department of Chemistry and Bioscience Aalborg UniversityNiels Bohrs vej8EsbjergDenmark

decision trees and their ensembles became quite popular for data analysis during the past *** of the main reasons for that is current boom in big data,where traditional statistical methods(such as,e.g.,multiple linear regression)are not very ***,in chemometrics these methods are still not very widespread,first of all because of several limitations related to the ratio between number of variables and *** paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and *** will try to consider all important aspects including optimization and validation of models,evaluation of results,treating missing data and selection of most important *** performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.

关键词： NIR spectroscopy decision trees Classification and regression trees Random forests

Effective use of FibroTest to generate decision trees in hepatitis C

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World Journal of Gastroenterology 2009年第21期15卷 2617-2622页

作者： Dana Lau-Corona Luís Alberto Pineda Héctor Hugo Avilés Gabriela Gutiérrez-Reyes Blanca Eugenia Farfan-Labonne Rafael Núez-Nateras Alan Bonder Rosalinda Martínez-García Clara Corona-Lau Marco Antonio Olivera-Martínez Maria Concepción Gutiérrez-Ruiz Guillermo Robles-Díaz David Kershenobich Department of Experimental Medicine School of MedicineUniversidad Nacional Autonoma de MexicoGeneral Hospital of Mexico Department of Computer Sciences Institute for Applied Mathematics and SystemsUniversidad Nacional Autonoma de Mexico Department of Gastroenterology Lomas Altas Clinic

AIM： To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS： We used the C4.5 classification algorithm to construct decision trees with data from 261 patients with chronic hepatitis C without a liver biopsy. The FibroTest attributes of age, gender, bilirubin, apolipoprotein, haptoglobin, α2 macroglobulin, and γ-glutamyl transpeptidase were used as predictors, and the FibroTest score as the target. For testing, a 10-fold cross validation was used.RESULTS： The overall classification error was 14.9% （accuracy 85.1%）. FibroTest＇s cases with true scores of FO and F4 were classified with very high accuracy （18/20 for FO, 9/9 for FO-1 and 92/96 for F4） and the largest confusion centered on F3. The algorithm produced a set of compound rules out of the ten classification trees and was used to classify the 261 patients. The rules for the classification of patients in FO and F4 were effective in more than 75% of the cases in which they were tested.CONCLUSION： The recognition of clinical subgroups should help to enhance our ability to assess differences in fibrosis scores in clinical studies and improve our understanding of fibrosis progression,

关键词： Hepatitis C FibroTest decision trees C4.5algorithm Non-invasive biomarkers

A Preliminary Study of Interpreting CNNs Using Soft decision trees

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国际计算机前沿大会会议论文集 2022年第1期 152-162页

作者： Qilong Zhao Yue Wang School of Information Central University of Finance and EconomicsBeijingChina

decision trees can be used to enhance the interpretability of neural *** this work,we compare the classification and interpretability performance of the normal decision tree and a type of soft decision tree when they are used to interpret the decision paths of CNN *** the help of feature visualization and human-labeled features,we demonstrate that the soft decision trees identify more consistent features while maintaining much higher classification performance than the normal decision tree.

关键词： CNNs Interpretability decision trees

Fuzzy ID3 decision trees for Data Classification:Application in Meteorology and Atmospheric Sciences

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Fuzzy ID3 Decision Trees for Data Classification:Application...

基于decision trees的配电网中性点不接地系统选线

2010 The 3rd International Conference on Computational Intelligence and Industrial Application (PACIIA2010)

作者： Chih-Chiang Wei Department of Information Management Toko University

The forecast of hourly precipitations in typhoon seasons has received much attention in recent years. It is important in meteorology and atmospheric sciences. This paper presents a novel algorithm,Fuzzy ID3 decision trees （FIDs）,for the precipitations during typhoon periods for a reservoir watershed. The FIDs algorithm was constructed as the quantitative precipitation forecast （QPF） model. This study also constructed the traditional average statistical model （AVS） to compare with the performance by FIDs model. The steps of flowchart involve collecting typhoon data,preprocessing the typhoon patterns,building QPF models,and training and testing the models. The developed methodology was applied to the Shihmen Reservoir watershed in Tahan River Basin. The results obtained from both FIDs and AVS models including the analysis of the 1-,3-,and 6-hr accumulated rainfalls during typhoon periods. The results showed that the superior relative mean square error （RMSE） performance by using FIDs in contrast to those by using traditional AVS. Consequently,the FIDs model demonstrated its feasibility.

关键词： decision trees Fuzzy Typhoon Prediction.

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云南电业 2024年第1期 6-10,18页

作者：黄贤明冯兆湖南工业大学计算机学院湖南株洲412007

针对配电网中性点不接地系统,使用录波数据解决单相接地选线不准的难题,提出了基于decision trees算法进行单相接地选线的方法.使用PSCAD仿真10kV配电网单相接地故障得到录波数据,转换成通用格式,提取稳态数据并进行预处理,通过对比多... 详细信息

针对配电网中性点不接地系统,使用录波数据解决单相接地选线不准的难题,提出了基于decision trees算法进行单相接地选线的方法.使用PSCAD仿真10kV配电网单相接地故障得到录波数据,转换成通用格式,提取稳态数据并进行预处理,通过对比多个机器学习分类算法,选择基于decision trees算法进行单相接地选线.选择的decision trees算法训练准确度高达99.99%,且模型小,训练速度快.将训练后的模型导出,使用实际的故障录波数据进行验证,选线准确度高达95%以上.结果表明该方法易于实现,准确度高,适合在实际中应用.

关键词：中性点不接地系统 decision trees 单相接地选线机器学习

Efficient privacy-preserving classification construction model with differential privacy technology

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Journal of Systems Engineering and Electronics 2017年第1期28卷 170-178页

作者： Lin Zhang Yan Liu Ruchuan Wang Xiong Fu Qiaomin Lin College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003China

To address the problem of privacy disclosure during data mining, a new privacy-preserving decision tree classification construction model based on a differential privacy-protection mechanism is presented. An efficient classifier that uses feedback to add two types of noise via Laplace and exponential mechanisms to perturb the calculation results are introduced to the construction algorithm that provides a secure data access interface for users. Different split solutions for attributes of continuous and discrete values are provided and used to optimize the search scheme to reduce the error rate of the classifier. By choosing an available quality function with lower sensitivity for making decisions and improving the privacy budget allocation methods, the algorithm effectively resists malicious attacks that depend on the background knowledge. The potential problem of obtaining personal information by guessing unknown sensitive nodes of tree-type data is solved correspondingly. The better privacy preservation and accuracy of this new algorithm are shown by simulation experiments. © 1990-2011 Beijing Institute of Aerospace Information.

关键词： Budget control Data mining decision trees trees (mathematics)

Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

Taiga: Performance Optimization of the C4.5 decision Tree Construction Algorithm

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Tsinghua Science and Technology 2016年第4期21卷 415-425页

作者： Yi Yang Wenguang Chen Department of Computer Science and Technology Tsinghua University Technology Innovation Center at Yinzhou Yangtze Delta Region Institute of Tsinghua University

Classification is an important machine learning problem, and decision tree construction algorithms are an important class of solutions to this problem. RainForest is a scalable way to implement decision tree construction algorithms. It consists of several algorithms, of which the best one is a hybrid between a traditional recursive implementation and an iterative implementation which uses more memory but involves less write operations. We propose an optimized algorithm inspired by RainForest. By using a more sophisticated switching criterion between the two algorithms, we are able to get a performance gain even when all statistical information fits in memory. Evaluations show that our method can achieve a performance boost of 2.8 times in average than the traditional recursive implementation.

关键词： C4.5 RainForest decision trees machine learning performance optimization