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能源与人工智能(英文) 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 equipment.The system response to disturbances must be secure and predictable to avoid power blackouts.The system response can be simulated in the time domain.However,this dynamic security assessment(DSA)is not computationally tractable in real-time.Particularly promising is to train Decision Trees(DTs)from machine learning as interpretable classifiers to predict whether the systemwide responses to disturbances are secure.In most research,selecting the best DT model focuses on predictive accuracy.However,it is insufficient to focus solely on predictive accuracy.Missed alarms and false alarms have drastically different costs,and as security assessment is a critical task,interpretability is crucial for operators.In this work,the multiple objectives of interpretability,varying costs,and accuracies are considered for DT model selection.We propose a rigorous workflow to select the best classifier.In addition,we present two graphical approaches for visual inspection to illustrate the selection sensitivity to probability and impacts of disturbances.We propose cost curves to inspect selection combining all three objectives for the first time.Case 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 compromises.The proposed approach scales well with larger systems and can be used for models beyond DTs.Hence,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

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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 decade.One 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 efficient.However,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 observations.This paper presents several examples on how Decision Trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification.We will try to consider all important aspects including optimization and validation of models,evaluation of results,treating missing data and selection of most important variables.The 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

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Nitrogen removal influence factors in A/O process and Decision Trees for nitrification/denitrification system

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Journal of Environmental Sciences 2004年第6期16卷 901-907页

作者： MAYong PENGYong-zhen WANGShu-ying WANGXiao-lian SchoolofMunicipalandEnvironmentalEngineering HarbinInstituteofTechnologyHarbin150090China DepartmentofEnvironmentalEngineering BeijingUniversityofTechnologyBeijing100022China

In order to improve nitrogen removal in anoxic/oxic(A/O) process effectively for treating domestic wastewaters, the influence factors, DO(dissolved oxygen), nitrate recirculation, sludge recycle, SRT(solids residence time), influent COD/TN and HRT(hydraulic retention time) were studied. Results indicated that it was possible to increase nitrogen removal by using corresponding control strategies, such as, adjusting the DO set point according to effluent ammonia concentration; manipulating nitrate recirculation flow according to nitrate concentration at the end of anoxic zone. Based on the experiments results, a knowledge-based approach for supervision of the nitrogen removal problems was considered, and Decision Trees for diagnosing nitrification and denitrification problems were built and successfully applied to A/O process.

关键词： A/O process nitrification denitrification nitrogen removal Decision Trees

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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 networks.In 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 networks.With 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

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Fuzzy ID3 Decision Trees for Data Classification:Application in Meteorology and Atmospheric Sciences

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

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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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One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

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Frontiers of Information Technology & Electronic Engineering 2022年第2期23卷 278-290页

作者： Minggang DONG Ming LIU Chao JING School of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems,the skewed distribution of multiclass data poses a major challenge to machine learning algorithms.To tackle such issues,we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance(OAHD).Two crucial elements are included in OAHD.First,the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD,thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance problem.Second,for the multiclass imbalance problem,the distribution and the number of distinct classes are taken into account,and a modified Gini index is designed.Moreover,we give theoretical proofs for the properties of OAHD,including skew insensitivity and the ability to seek a purer node in the decision tree.Finally,we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning(KEEL)repository and the University of California,Irvine(UCI)repository.Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known Decision Trees in terms of Precision,F-measure,and multiclass area under the receiver operating characteristic curve(MAUC).Moreover,through statistical analysis,the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other Decision Trees.

关键词： Decision Trees Multiclass imbalanced learning Node splitting criterion Hellinger distance One-against-all scheme

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A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

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Computers, Materials & Continua 2022年第2期70卷 2971-2989页

作者： Jose Escorcia-Gutierrez Jordina Torrents-Barrena Margarita Gamarra Natasha Madera Pedro Romero-Aroca Aida Valls Domenec Puig Electronic and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Ophthalmology Service Universitari Hospital Sant JoanInstitut de Investigacio Sanitaria Pere VirgiliReus43201Spain Departament d’Enginyeria Informàtica i Matemàtiques Escola Tècnica Superior d’EnginyeriaUniversitat Rovira i VirgiliTarragona43007Spain

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, Decision Trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational power.Our method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

关键词： Diabetic retinopathy artificial neural networks Decision Trees support vector machines feature selection retinal vasculature segmentation

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Deep forest

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National Science Review 2019年第1期6卷 74-86页

作者： Zhi-Hua Zhou Ji Feng National Key Laboratory for Novel Software Technology Nanjing University

Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as Decision Trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as Decision Trees and boosting machines,we conjecture that the success of deep neural networks owes much to three characteristics, i.***.layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand,our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation.

关键词： deep forest deep learning machine learning ensemble methods Decision Trees

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Cloud-Based Diabetes decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： Shabib Aftab Saad Alanazi Munir Ahmad Muhammad Adnan Khan Areej Fatima Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,Decision Trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

关键词： Machine learning fusion articial neural network Decision Trees naïve Bayes diabetes prediction

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Secure and Privacy-Preserving decision Tree Classification with Lower Complexity

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Journal of Communications and Information Networks 2020年第1期5卷 16-25页

作者： Liang Xue Dongxiao Liu Cheng Huang Xiaodong Lin Xuemin(Sherman)Shen Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada School of Computer Science University of GuelphGuelph N1G 2W1Canada Department of Electrical and Computer EngineeringUniversity of WaterlooWaterloo N2L 3G1Canada

As a widely-used machine-learning classifier,a decision tree model can be trained and deployed at a service provider to provide classification services for clients,e.g.,remote diagnostics.To address privacy concerns regarding the sensitive information in these services(i.e.,the clients’inputs,model parameters,and classification results),we propose a privacy-preserving decision tree classification scheme(PDTC)in this paper.Specifically,we first tailor an additively homomorphic encryption primitive and a secret sharing technique to design a new secure two-party comparison protocol,where the numeric inputs of each party can be privately compared as a whole instead of doing that in a bit-by-bit manner.Then,based on the comparison protocol,we exploit the structure of the decision tree to construct PDTC,where the input of a client and the model parameters of a service provider are concealed from the counterparty and the classification result is only revealed to the client.A formal simulation-based security model and the security proof demonstrate that PDTC achieves desirable security properties.In addition,performance evaluation shows that PDTC achieves a lower communication and computation overhead compared with existing schemes.

关键词： Decision Trees data privacy model privacy secure comparison machine-learning classification

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