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Application of decision tree algorithm for classification and identification of natural minerals using SEM-EDS

COMPUTERS & GEOSCIENCES 2015年 80卷 38-48页

作者： Akkas, Efe Akin, Lutfiye Cubukcu, H. Evren Artuner, Harun Hacettepe Univ Dept Geol Engn TR-06800 Ankara Turkey Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey

A mineral is a natural, homogeneous solid with a definite chemical composition and a highly ordered atomic arrangement. Recently, fast and accurate mineral identification/classification became a necessity. Energy Dispersive X-ray Spectrometers integrated with Scanning Electron Microscopes (SEM) are used to obtain rapid and reliable elemental analysis or chemical characterization of a solid. However, mineral identification is challenging since there is wide range of spectral dataset for natural minerals. The more mineralogical data acquired, time required for classification procedures increases. Moreover, applied instrumental conditions on a SEM-EDS differ for various applications, affecting the produced X-ray patterns even for the same mineral. This study aims to test whether C5.0 decision tree is a rapid and reliable method algorithm for classification and identification of various natural magmatic minerals. Ten distinct mineral groups (olivine, Orthopyroxene, clinopyroxene, apatite, amphibole, plagioclase, K-feldspar, zircon, magnetite, biotite) from different igneous rocks have been analyzed on SEM-EDS. 4601 elemental X-ray intensity data have been collected under various instrumental conditions. 2400 elemental data have been used to train and the remaining 2201 data have been tested to identify the minerals. The vast majority of the test data have been classified accurately. Additionally, high accuracy has been reached on the minerals with similar chemical composition, such as olivine ((Mg,Fe)(2)[SiO4]) and orthopyroxene ((Mg,Fe)(2)[SiO6]). Furthermore, two members from amphibole group (magnesiohastingsite, tschermakite) and two from clinopyroxene group (diopside, hedenbergite) have been accurately identified by the decision tree algorithm. These results demonstrate that C5.0 decision tree algorithm is an efficient method for mineral group classification and the identification of mineral members. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Mineral classification Energy dispersive spectrometry decision tree algorithm Scanning Electron Microscope C5.0 decision tree

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Classification method of English micro-course resources on MOOC platform based on improved decision tree algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 34-43页

作者： Wang, Juan Wuhan Tech Coll Commun Wuhan Hubei Peoples R China

To solve the problem of inaccurate classification results of current educational resource classification methods, a MOOC micro-class resource classification method based on improved decision tree algorithm is proposed. Firstly, adaptive reconstruction technology and feature sequence detection technology are used to build a storage structure model for optimising unbalance data of cloud resource distribution space on MOOC platform. Then, the information gain rate of each attribute is determined by using the information entropy of each attribute and the decision tree is constructed. Finally, the improved information entropy is used to optimise the decision tree to realise the classification of English micro-lesson resources. The experimental results show that the AUC value of the classification results of the proposed method is higher than 0.85, and the precision rate, recall rate and F-scale value are higher than 0.7, which indicates a high accuracy of resource classification.

关键词： decision tree algorithm MOOC platform English micro-course resources classification method information entropy information gain rate

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Financial Management and decision Based on decision tree algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 2869-2884页

作者： Jin, Maozhu Wang, Hua Zhang, Qian Luo, Cheng Sichuan Univ Sch Business Chengdu Sichuan Peoples R China Chengdu Agr Coll Econ & Management Sch Chengdu 610064 Sichuan Peoples R China

In order to make university financial management more scientific, technologies such as data warehouse, data mining technology and decision support system are applied to college affairs decision-making. Aimed at a large amount of financial data generated by the financial management system in colleges and universities, the financial management and decision-making in colleges and universities are realized based on the decision tree algorithm. It provides effective support for the management of colleges and universities. The advantages and disadvantages of clustering algorithm and classification algorithm are analyzed. An improved C4.5 decision tree algorithm based on metric is proposed. Combined with data warehouse, data mining and analysis technology, data-driven thinking is adopted to establish university financial budget and model. Financial management and decision-making in colleges and universities are realized. It is applied to the financial project budget execution progress early warning, and carries on the experiment analysis and the result research. A complete financial data warehouse is built. Data aggregation, data analysis and data mining can reduce the amount of data information of the system. The result shows that this method makes college financial management and decision-making more convenient and effective.

关键词： University Financial management Data mining Data analysis decision tree algorithm

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Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and artificial neural network approach

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NATURAL HAZARDS 2015年第2期75卷 1349-1368页

作者： Dutta, Debashree Chaudhuri, Sutapa Univ Calcutta Dept Atmospher Sci Kolkata 700019 India

The endeavor of the present research is to nowcast the spatial visibility during fog over the airport of Kolkata (22.6 degrees N;88.4 degrees E), India, with artificial neural network (ANN) model. The identification of dominant parameters influencing the visibility during wintertime (November-February) fog over the region is made using the decision tree algorithm. The decision tree is constructed by computing the entropy of the parameters collected during the period from 2001 to 2011. The parameters having minimum entropy are selected as the most useful parameters because it has maximum certainty in influencing the visibility. The result reveals that the moderate range of NO2 (67-134 mu g/m(3)) is the most dominant parameter compared with other parameters that influence the visibility during wintertime fog over Kolkata and is selected as the first node of the tree. The decision tree approach led to select five such parameters having minimum entropy for affecting maximum the visibility during fog over Kolkata airport. The selected parameters are NO2, wind speed, relative humidity, CO and temperature. ANN model is developed with the selected parameters as the input in the form of multilayer perceptron with back propagation learning technique for forecasting the 3 hourly visibility during wintertime fog over Kolkata airport. The result reveals that the forecast of visibility of different categories is possible with ANN model. However, the best forecast is obtained for very dense visibility within the 50 m horizontal distance. The result is validated with observation, and the forecast error is estimated.

关键词： decision tree algorithm Artificial neural network model Multilayer perceptron Forecast skill Entropy NO2 Wind speed Relative humidity CO and temperature

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A decision tree algorithm for forest fire prediction based on wireless sensor networks

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2020年第4期13卷 422-430页

作者： Gao, Demin Xin, Jie Zhang, Fuquan Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA McMaster Univ Elect & Comp Engn Dept Hamilton ON Canada

Forest fire poses a significant threat not only to the natural environment and ecological systems but also to the safety of human life and property. Combined with new technologies, a decision tree algorithm is proposed for forest fire prediction, in which wireless sensor networks technology is utilised to transmit data and predict the ignition of the forest. There are four meteorological parameters as part of training data, containing temperature, relative humidity, wind speed, and daily precipitation, while the other part is prediction results of Forest Weather Index system. The decision tree generated by our system could classify these parameters from the most significant to the least significant so that it can better foretell fire occurrence. The analysis of prediction results shows that our system is effective.

关键词： decision tree algorithm forest fire prediction wireless sensor networks WSNs

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Retrieval method for English teaching resources based on decision tree algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2023年第4期73卷 231-237页

作者： Du, Jinyan Xinxiang Vocat & Tech Coll Dept Fundamental Courses Xinxiang Henan Peoples R China

A decision tree algorithm based English teaching resource retrieval method is proposed to address the problems of current resource retrieval methods, including low recall and precision, as well as longer retrieval time. Firstly, the word frequency statistical method is used to extract the features of teaching resources. Then, the C4.5 algorithm, also known as the decision tree algorithm, is applied to classify English teaching resources based on information gain. Finally, the Sussan concept similarity algorithm is used to calculate the similarity between the retrieved content and the database content, and the similarity values are sorted from high to low to select the optimal English teaching resource retrieval result. The experimental results show that the proposed method always maintains a recall rate between 98.0% and 99.0%, surpassing 98.00% in precision and the shortest retrieval time is only 8.15 seconds.

关键词： decision tree algorithm English teaching resources feature extraction semantic similarity resource retrieval

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Teaching Quality Evaluation and Scheme Prediction Model Based on Improved decision tree algorithm

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第10期13卷 146-157页

作者： Jia, Sujuan Pang, Yajing Hebei Univ Sci & Technol Dept Teaching Affairs Shijiazhuang 050018 Hebei Peoples R China Hebei Univ Sci & Technol Dept Dev Planning Shijiazhuang 050018 Hebei Peoples R China

Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner's personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learner's personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner's personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statistical data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed.

关键词： decision tree algorithm statistical analysis teaching quality evaluation teaching direction prediction

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Data-driven Weld Nugget Width Prediction with decision tree algorithm

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Procedia Manufacturing 2017年 10卷 1009-1019页

作者： Ahmed F. Kim K.-Y. Department of Industrial and Systems Engineering Wayne State University Detroit MI 48202 United States

This paper presents the capability of a decision tree algorithm to realize a data-driven resistance spot welding (RSW) weldability prediction. Although RSW provides commendable advantages, such as low cost and high speed/high volume operations, the RSW processes are often inconsistent and these significant inconsistencies are a well-known reliability issue. RSW process and data challenges including inconsistency often hinder the utilization of the data-driven weldability prediction. In this paper, we apply a decision tree algorithm on the RSW dataset collected from an automotive OEM to plot regression trees and to extract decision rules for the weld nugget width prediction. With three RSW test datasets, we conclude that the decision trees help in predicting the nugget width and in determining the impact of design and process parameters to the nugget width response variable. © 2017

关键词： Data-driven weldability prediction decision tree algorithm resistance spot welding weld nugget

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Regional Voltage Stability Prediction based on decision tree algorithm

Regional Voltage Stability Prediction based on decision tree...

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International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS)

作者： Teng Yun Huo Tengyu Liu Bing Tang Jing Zhang Zhongjie Shenyang Univ Technol Minist Educ Key Lab Special Elect Machine & High Voltage Appa Shenyang 110870 Liaoning Provin Peoples R China State Grid Tongliao Power Supply Co Tongliao 028000 Inner Mongolia Peoples R China

ISBN: (纸本)9781509004645

Because of the imperfection of the theory concerning the regional dynamic and static voltage stability evaluation problem and insufficient reliability of the history data retrieved from the FTU, a novel pattern-recognition based algorithm is proposed. The proposed algorithm utilizes advantage of the decision tree algorithm and a decision tree algorithm model was built for the purpose of evaluation of voltage stability status of the regional power grid. Three feature has been extracted for the recognition of the instability pattern. The training set and the test set data are both obtained from simulation data and outputs. By using the proposed method on IEEE 39-bus system and the predefined critical unstable situations, we obtained the decision tree algorithm model on the IEEE 39-bus system stability prediction model. After the simulation, the output data are compared with the original stability status. The experiment comparison shows that the decision tree algorithm model has achieved the acceptable accuracy for the assessment of voltage stability prediction of the regional power grid. The validity of the proposed assessment scheme as well as the adaptability and the accuracy of the classification model are verified by simulation results of IEEE 39-bus system..

关键词： decision tree algorithm Voltage stability IEEE 39-bus system Feature Extraction

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A Study On Evaluation of farmland fertility levels based on optimization of the decision tree algorithm

A Study On Evaluation of farmland fertility levels based on ...

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Liu He Zhang Xiaoxian Cao Liying Li Dexin Jilin Agr Univ Coll Informat & Technol Sci Changchun 130118 Jilin Peoples R China

ISBN: (纸本)9781467329644;9781467329637

Base on the organic matter, total nitrogen, available phosphorus and potassium content in the cultivated land of The 20 District in Dehui 20, it comes a study on using the decision tree ID3 algorithm for the evaluation of farmland fertility levels. Through experimental analysis, it gets the number of grade 6, grade sub- region soil nutrient content is similar or smaller differences, and significant differences among grades. Fertility Evaluation based on the ID3 algorithm farmland can be used for variable rate fertilization in the guidance of precision agriculture, to provide an effective division for farmland fertility level partition.

关键词： decision tree algorithm soil nutrients fertility level variable fertilization

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