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检索条件"主题词=Decision tree algorithm"

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A Method for Evaluating the Performance of Unmanned Aerial Navigation Systems Based on Fuzzy decision trees 13th

A Method for Evaluating the Performance of Unmanned Aerial N...

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13th China Satellite Navigation Conference (CSNC) - Digital Economy and Intelligent Navigation

作者： Chen, Changyuan Shi, Chunlin Liu, Teng Li, Haojie Li, Qiyang Ma, Li Informat Engn Univ Zhengzhou Peoples R China Northwestern Polytech Univ Xian Peoples R China Coll Aeronaut Engn Xian Peoples R China Changchun Univ Sci & Technol Changchun Peoples R China

ISBN: (纸本)9789811925887;9789811925870

In the process of application, unmanned aerial vehicles (UAVs) are faced with the threat of a complex electromagnetic environment, causing the airborne navigation system to fail to achieve ideal working conditions, and seriously restricting the overall effectiveness of the UAV system. Therefore, accurate assessment of UAV navigation system performance is an important prerequisite for scientific decision-making on the use of UAVs in complex environments in the future. In system design and experimentation, the more realistic electromagnetic interference environments that UAVs may be exposed to are difficult to build, resulting in insufficient data being collected to verify UAV navigation system performance. However, as real data from UAVs is continuously accumulated in various application scenarios at a later stage, it is necessary to explore a data mining-based method for UAV navigation system performance evaluation. In this study, a decision tree algorithm was used to construct a UAV navigation performance level classification assessment model. UAV performance was analysed through UAV navigation capability and survivability indicators. Finally, simulation experiments were conducted to further develop the construction and testing of the decision tree model. The experimental results showed that the model accuracy is above 90%, validating the feasibility of the method.

关键词： UAV navigation Electromagnetic environment Performance evaluation Data mining decision tree algorithm

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Exploratory analysis of methods for automated classification of clinical diagnoses in Veterinary Medicine 21

Exploratory analysis of methods for automated classification...

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25th International Database Applications and Engineering Symposium (IDAES)

作者： Tamburis, Oscar Masciari, Elio Fatone, Gerardo Univ Naples Federico II Dept Vet Med & Anim Prod Naples Italy Univ Naples Federico II Dept Comp Sci & Elect Engn Naples Italy

ISBN: (纸本)9781450389914

The present work describes the analysis conducted on the diagnoses made during the general physical examinations in the decade 2010-2020, starting from the DB of the EMR previously implemented in the University Veterinary Teaching Hospital at Federico II University of Naples. A decision tree algorithm was implemented to work out a predictive model for an effective recognition of neoplastic diseases and zoonoses for cats and dogs from Campania Region. The results achievable by data mining techniques for what concerns computer aided disease diagnosis and exploration of risk factors and their relations to diseases, show the increasing importance of Veterinary Informatics within the wider field of Biomedical and Health Informatics, and in particular its capacity to point out the existing connections between humans, animals, and surrounding environment, according to the One (Digital) Health perspective specifics.

关键词： Electronic Medical Record Veterinary Informatics decision tree algorithm Data Mining One Digital Health

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Learning classifier systems for user context learning

Learning classifier systems for user context learning

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IEEE Congress on Evolutionary Computation

作者： Shankar, A Louis, S Univ Nevada Dept Comp Sci & Engn ECSL Reno NV 89557 USA

ISBN: (纸本)0780393635

Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application program which is designed to learn a mapping from user-related contextual features to reminder actions. In this paper, we consider the feasibility of using a genetics-based machine learning technique, XCS, for the purpose of learning this mapping from a set of context features to reminder actions as a predictive data-mining task. We compare XCS's performance with a decision tree algorithm on this learning task and show that XCS outperforms the decision tree learner.

关键词： data mining genetic algorithms learning (artificial intelligence) personal computing user interfaces Sycophant XCS decision tree algorithm learning classifier systems learning user preferences machine learning predictive data mining task user context lea

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Fault Diagnosis of Power Transformers Based on Comprehensive Machine Learning of Dissolved Gas Analysis 20

Fault Diagnosis of Power Transformers Based on Comprehensive...

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20th IEEE International Conference on Dielectric Liquids (ICDL)

作者： Guo, Chenxi Dong, Ming Wu, Zhanyu Xi An Jiao Tong Univ State Key Lab Elect Insulat Power Equipment Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728117188

Currently, the commonly used fault diagnosis methods of power transformers are often difficult to deal with the ambiguity problems encountered in the troubleshooting process. Even with some artificial intelligent techniques already experimented, the results have not been not systematic compared and are far from real application. Therefore, this paper tries to make a thorough use of machine learning tools towards result data from dissolved gas analysis. This paper establishes a machine learning model based on dissolved gas analysis for internal fault diagnosis of power transformers, and makes a comparison between multiple machine learning methods. Firstly, an overall neural network model of transformer diagnosis based on the dissolved gas analysis is formed. Next, the performance of optimized BP neural network, probabilistic neural network (PNN), and decision tree algorithm is compared from the aspects of speed and accuracy. Furthermore, the case-based reasoning method based on the Euclidean distance and normalized energy intensity algorithm is employed to get the closest matching similar case to realize the transformer defect prediction and assistant decision-making. With actual examples verified, the case-based reasoning method can help detect the most likely abnormal causes of faulty transformers through providing the most similar matching case.

关键词： transformer fault diagnosis BP neural network probabilistic neural network decision tree algorithm case-based reasoning

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A Hybrid Model for Predicting the End-Point Phosphorus Content of Electric Arc Furnace

A Hybrid Model for Predicting the End-Point Phosphorus Conte...

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Symposium on Materials Processing Fundamentals at Annual Meeting of The-Minerals-Metals-Materials-Society (TMS)

作者： Chen, Chao Wang, Nan Chen, Min Northeastern Univ Sch Met Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9783030652531;9783030652524

A hybrid model based on the combination of k-means method, BP neural network and decision tree algorithm is proposed for predicting the end-point phosphorus content of electric arc furnace. The industrial data from electric arc furnace is filtered firstly by the box-plot method, and the processed data of end-point phosphorus content is classified into three clusters by k-means analysis method. Then, three BP neural networks with different parameters for each cluster are established to deal with the data overlapping problem and increase the model accuracy. In order to obtain the optimum prediction result, a new method combined with the posterior knowledge of dephosphorization ratio and the decision tree algorithm is employed. With this method, the results predicted, respectively, by the three different BP neural networks are merged according to the merging rule set established by decision tree algorithm and the identified result is taken as the final end-point phosphorus content. In comparison with the traditional BP neural network and deep layer neural network, the hybrid model increases the prediction accuracy of end-point P content to 97.8% with +/- 0.006% error range, and meanwhile for the error ranges of +/- 0.005% and +/- 0.004%, the prediction accuracy is 94.2% and 83.0%, respectively.

关键词： End-point phosphorus content BP neural network model K-means method Data overlapping decision tree algorithm

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An adaptive Technique based on Separability Index to classify PALSAR data 2

An adaptive Technique based on Separability Index to classif...

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2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall)

作者： Thakur, Vartika Ghildiyal, Pragati G. N. Prakash, Rishi Graph Era Univ Dept Elect & Commun Dehra Dun UK India

ISBN: (纸本)9781509034802

In this paper, we have developed an adaptive technique based on separability index for classification of PALSAR data. First of all this paper investigate the effect of sample size on accuracy assessment. Further, we have utilized minimum distance to mean classifier and studied the sample size varying from 20 to 500. A comprehensive analysis was made for different polarized images, i.e., HH, HV, VV and their combinations i.e., HH-HV, HV-VV, HH-VV, and HH-HV-VV. Further, to increase the classification accuracy, separability index, which tells about the idea of class separation in different polarization was analyzed. A decision tree based on separability index was developed and adaptive statistical approach based on mean and standard deviation was used to classify PALSAR data. Good overall accuracy and class wise accuracy was observed with this adaptive technique.

关键词： Polarimetric Synthetic Aperture Radar Supervised classification Minimum distance to mean Separability Index decision tree algorithm Genetic algorithm

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Prescriptive Faculty Performance Analysis: A Case at the Onset of Covid-19 Pandemic

Prescriptive Faculty Performance Analysis: A Case at the Ons...

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IEEE Region 10 Conference (TENCON)

作者： Benosa, Brenda Caguiat, Ma Rowena Raguin De La Salle Univ Coll Comp Studies Manila Philippines

ISBN: (纸本)9781665495325

The challenges of internationalization, the diversion to outcome-based education, and the emergence of the COVID-19 pandemic triggered a growing demand for quality educators. Hence, educational institutions shall ensure continuous evaluation of faculty performance and use its data as a tool to capacitate learning providers and enhance instruction in the classroom. Using the identified performance indicators, this study aims to elicit insights from the dataset extracted from the Faculty Performance Evaluation System (FPES) of the Camarines Sur Polytechnic Colleges (CSPC) to understand how the students perceived their respective instructors' performance levels prior to and at the onset of the COVID-19 pandemic. Generated patterns were uncovered using descriptive analysis based on the students' ratings. Meanwhile, the students' comments, suggestions, and recommendations were analyzed using Sentiment Analysis through TextBlob. The same dataset was further examined to recommend a prescribed action using a supervised learning method (decision tree algorithm). With 98% model accuracy, faculty performance testing dataset were provided with prescribed actions with the following rules: Outstanding & Very Satisfactory Ratings = Re-Hire/No Action Needed;Satisfactory = Mentorship;Unsatisfactory & Poor = Re-Training & Re-Evaluation. The study discovered a decline in the faculty performance evaluation results at the onset of the COVID-19 pandemic. However, the students' sentiments were considerate to the faculty's endeavor as most of its polarity scores fell under "positive." Recommendations to strengthen and boost faculty performance were incorporated based on the findings of the prescriptive analysis.

关键词： Data Mining decision tree algorithm Sentiment Analysis Faculty Performance COVID-19 Pandemic Education

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A Critical Study of Classification algorithms Using Diabetes Diagnosis 6

A Critical Study of Classification Algorithms Using Diabetes...

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IEEE 6th International Advance Computing Conference (IACC)

作者： Srikanth, Panigrahi Deverapalli, Dharmaiah VNR VJIET Dept Informat Technol Hyderabad Telagana India Shri Vishnu Engn Coll Woman Dept Informat Technol Bhimavaram India

ISBN: (纸本)9781467382861

This paper predicts the Diabetes Disease based on Data Mining Techniques of Classification algorithms. Classification algorithm and tools may reduce heavy work on Doctors. In this paper Evaluated as Classification algorithms for the Classify of some Diabetes Disease Patient Datasets. Data Mining is one of the main algorithm is Classification. Classification algorithm Examine of the decision tree algorithm, Byes algorithm and Rule based algorithm. These algorithms are evaluate Error Rates and identify of the patients based evolution Function of the measure the accurate results.

关键词： Pima Diabetes Disease Data set Classification algorithm decision tree algorithm Byes algorithms and Rules

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Improving Arabic Text Categorization using decision trees

Improving Arabic Text Categorization using Decision Trees

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1st International Conference on Networked Digital Technologies (NDT 2009)

作者： Harrag, Fouzi El-Qawasmeh, Eyas Pichappan, Pit Farhat ABBAS Univ Dept Comp Sci Setif 19000 Algeria JUST Univ Comp Sci Dept Amman 25000 Jordan Al Imam Univ Dept Comp Sci Riyadh Saudi Arabia

ISBN: (纸本)9781424446148

This paper presents the results of classiying Arabic text documents using a decision tree algorithm. Experiments are performed over two self collected data corpus and the results show that the suggested hybrid approach of Document Frequency Thresholding using an embedded information gain criterion of the decision tree algorithm is the preferable feature selection criterion. The study concluded that the effectiveness of the improved classifier is very good and gives generalization accuracy about 0.93 for the scientific corpus and 0.91 for the literary corpus and we also conclude that the effectiveness of the decision tree classifier was increased as we increase the training size, and the nature of the corpus has such a influence on the classifier performance.

关键词： Text Mining decision tree algorithm Text Categorization Arabic Corpus Feature Selection

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Detecting Pantana Phyllostachysae Using GF-2 Images 2

Detecting Pantana Phyllostachysae Using GF-2 Images

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2nd International Conference on Frontiers of Sensors Technologies (ICFST)

作者： Wu, Hongzhi Yang, Dong Shao, Bo Qiao, Rui Sun, Linwen Shi, Feng Shandong Inst Dev Strategy Sci & Technol Jinan Shandong Peoples R China Univ Waterloo Dept Math Waterloo ON Canada

ISBN: (纸本)9781509048601

Forest disturbances caused by Pantana phyllostachysae caused the death of extensive bamboos in Hubei province, China in 2015. Field survey is time-consuming and at higher cost to satisfy the forest management requirements. Satellite remote sensing technology having the characteristics of landscape of coverage, convenient, and fast in formation acquisition, is one of the most important and most effective ways of discriminating the damage. Based on decision tree algorithm, the study compared the results on the condition of segmentation variables on different scales and colors and proposed that GF-2 image are suitable for discriminating the damage as an alternative of field survey.

关键词： GF2 image decision tree algorithm detection

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