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Financial Management and decision Based on decision tree algorithm

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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Research on optimization and application of Spark decision tree algorithm under cloud-edge collaboration

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 8833-8854页

作者： Wang, Suzhen Jia, Zhiting Cao, Ning Hebei Univ Econ & Business Shijiazhuang Hebei Peoples R China Wuxi Vocat Coll Sci & Technol Sch Artificial Intelligence Wuxi Jiangsu Peoples R China

This paper optimizes and parallelizes the C4.5 algorithm on the Spark platform under cloud-edge collaboration. First, the information entropy calculation in the definition of boundary points and Fayyad's theorem is improved by the Gini index, which reduces the number of information entropy calculations for selecting segmentation points in the traditional C4.5 algorithm continuous attribute discretization operation and simplifies the calculation formula, thus reducing the execution time of the algorithm. On this basis, the CFS algorithm is introduced to optimize the calculation of the information gain ratio, to facilitate the selection of better attributes for decision tree partitioning. The improved C4.5 algorithm is then parallelized in Spark platform. In this paper, we choose the "intelligent door guard" of cloud-edge collaboration under the epidemic prevention and control management context as the application scenario for experimental verification and make timely risk assessment for those who want to enter the place and give them a response whether they are allowed to enter or not. Experiments show that the improved parallel C4.5 algorithm reduces the running time and raises the accuracy of the algorithm, compared with the traditional C4.5 algorithm and previous improvements to it. In the application scenario of cloud-edge collaboration, the improved algorithm in this paper decreases the time delay and increases the inspection efficiency.

关键词： C4 5 algorithm cloud-edge collaboration decision tree algorithm edge computing information gain ratio

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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 Study on the Application of the decision tree algorithm in Psychological Information of Vocational College Students

A Study on the Application of the Decision Tree Algorithm in...

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International Conference on Engineering Technology and Application (ICETA)

作者： Cheng, Dongmei Li, Tan Niu, Lihua Langfang Hlth Vocat Coll Dept Publ Basic Langfang Hebei Peoples R China

This paper discusses the basic operating principle and the development status of data mining technology, analyzes the insufficiency of the existing psychological management system, and proposes the development trend of psychological health education in colleges. According to an analysis on factors affecting college students' mental health and the deviation between the reality and the current number of students with psychological abnormality, this paper studies the application of data mining technology and puts forward a system based on data mining that combines the classified data mining technology with the existing psychological management system.

关键词： decision tree algorithm psychological information of college students data mining

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The Construction of English New Words Corpus Based on decision tree algorithm 4th

The Construction of English New Words Corpus Based on Decisi...

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4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND)

作者： Gao, Hongxia Beihua Univ Teachers Coll Jilin 132013 Jilin Peoples R China

ISBN: (纸本)9783031243516;9783031243523

An English word is a sound or a combination of sounds that we make with the vocal organs;words are representative, they represent some meaning, and they help humans communicate;words are part of the human language communication system. In short, words are the smallest and meaningful language units that humans can use independently. This paper aims to study the English neologism corpus based on decision tree algorithm. Aiming at the problems of low internal cohesion of words in the new word recognition algorithm of point mutual information and adjacency entropy, many high threshold invalid phrases and low threshold new phrases that exist in the single threshold setting of point mutual information, an improved multi-word point is proposed. A new word extraction algorithm for English of the year based on mutual information and adjacency entropy. In the preprocessing stage, according to the characteristics of English new words, it is further filtered, and the point mutual information is expanded into multi-word point mutual information, and the new words are extracted by setting double thresholds and adjacency entropy. In the recognition algorithm, this paper regards it as a classification problem to solve, analyzes English new words, and uses cosine similarity and path distance similarity to quantify word form and word meaning features. For phrase pairs containing acronyms, this paper An algorithm for identifying acronyms by rules is proposed, and a decision tree suitable for English new word recognition is constructed based on the features of word form and word meaning. Experiments show that the data of the algorithm in this paper is better than the traditional algorithm, the P value reaches 33.8%, and the accuracy rate is greatly improved. Compared with the word sense feature recognition algorithm, the method in this paper has a 4% improvement in accuracy.

关键词： decision tree algorithm Synonym Identification New Words New Word Corpus

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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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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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Simulation of Regional Financial and Economic Development decision Model Based on decision tree algorithm 5th

Simulation of Regional Financial and Economic Development De...

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5th International Conference on Big Data and Security (ICBDS)

作者： Wu, Xueliang Zou, Xiao Zhanjiang Univ Sci & Technol Zhanjiang 524088 Guangdong Peoples R China

ISBN: (纸本)9789819743896;9789819743902

Regional financial difference is an objective economic phenomenon in the process of regional economic development, and the financial operation must have the characteristics of regional imbalance. However, excessive dependence on foreign trade and low-cost resources has brought many problems to China's population, resources, environment and sustainable economic growth. How to promote the high-quality development of the regional economy of the real economy and make the financial industry become the "booster" to promote the development of the real economy has become the key research direction in the field of economic research. The purpose of this paper is to establish a regional financial and economic development decision model based on decision tree algorithm to solve the problems in the economy. In the field of regional financial economy based on decision tree algorithm, the optimization of economic benefits has become the core of regional economic management and the ultimate goal of economic management. Finally, the regional economic decision-making plan will be put forward according to the set objectives, and the plan will directly affect the economic benefits and enterprise income.

关键词： decision tree algorithm Regional finance Economic development decision-making model

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Teaching Reform of Financial Management Major in Applied Undergraduate Based on decision tree algorithm 17

Teaching Reform of Financial Management Major in Applied Und...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Xia, Juan Jiang, HaiYan HeBei Normal Univ Nationalities Business Sch Chengde City Hebei Peoples R China

ISBN: (纸本)9781728186160

With the continuous expansion of the enrollment scale of colleges and universities, the management of colleges and universities is becoming more and more difficult, and the corresponding data accumulation is also more and more. The key to the development of colleges and universities is to analyze the factors affecting the cultivation of students and improve the methods of students' training. According to the teaching data of colleges and universities, we can use the data mining technology to transform the existing teacher management data into useful information, and through data analysis to explore the internal factors affecting students' academic performance, so as to provide theoretical guidance for improving teaching methods. The results show that in order to improve the level of education and training in Colleges and universities, it is necessary to analyze the teaching quality of financial management course according to the needs of teaching. In order to optimize the course testing link, improve teaching methods and do a good job in teaching evaluation. We can guide the adjustment of students' cultural theory and practice courses through the analysis of students' performance data, and determine a reasonable student training program. This paper proposes to use decision tree algorithm to generate effective information to help managers to make decisions and financial budget management, so as to carry out experiments.

关键词： decision tree algorithm Financial Management Teaching Reform Application Research

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Application of decision tree algorithm in Lumber Hierarchies

Application of Decision Tree Algorithm in Lumber Hierarchies

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1st International Conference on Intelligent System and Applied Material (GSAM 2012)

作者： Guo, Dan Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China

ISBN: (纸本)9783037853689

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

关键词： decision tree algorithm ID3 hierarchies

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