检索结果-内蒙古大学图书馆

construction of Risk Prediction Models for Enterprise Finance Sharing Operations Using K-Means and c4.5 algorithms

INTERNATIONAL JOURNAL OF cOMPUTATIONAL INTELLIGENcE SYSTEMS 2024年第1期17卷 208-208页

作者： Pan, chun Changjiang Inst Technol Fac Econ & Management Wuhan 430212 Peoples R China

The evaluation of financial sharing centres in enterprises typically relies on outdated financial data, lacks comprehensive assessment, and presents risks such as employee misconduct. To address these challenges, we propose a risk prediction model for enterprise financial sharing operations based on the K-means clustering algorithm for performance evaluation and the c4.5 algorithm for managing employee risks. Our approach enhances the accuracy and objectivity of performance evaluation while improving the efficiency of personnel risk management. Results indicate that the K-means algorithm classifies employee performance into five levels, facilitating comprehensive performance evaluation. Furthermore, through risk management optimisation, accuracy and recall rates increase to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test sets, respectively. Practical application of our methodology and model in A Group's financial sharing centre demonstrates their effectiveness and potential for enhancing the operation and management of enterprise financial sharing centres.

关键词： Enterprise financial sharing Risk prediction models K-means clustering algorithm c4.5 algorithm

来源：评论

学校读者我要写书评

暂无评论

credal-c4.5: Decision tree based on imprecise probabilities to classify noisy data

引用

EXPERT SYSTEMS WITH APPLIcATIONS 2014年第10期41卷 4625-4637页

作者： Mantas, carlos J. Abellan, Joaquin Univ Granada ETSI Informat Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

In the area of classification, c4.5 is a known algorithm widely used to design decision trees. In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A modification of c4.5, called credal-c4.5, is presented in this paper. This new procedure uses a mathematical theory based on imprecise probabilities, and uncertainty measures. In this way, credal-c4.5 estimates the probabilities of the features and the class variable by using imprecise probabilities. Besides it uses a new split criterion, called Imprecise Information Gain Ratio, applying uncertainty measures on convex sets of probability distributions (credal sets). In this manner, credal-c4.5 builds trees for solving classification problems assuming that the training set is not fully reliable. We carried out several experimental studies comparing this new procedure with other ones and we obtain the following principal conclusion: in domains of class noise, credal-c4.5 obtains smaller trees and better performance than classic c4.5. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Imprecise probabilities Imprecise Dirichlet Model Uncertainty measures credal decision trees c4.5 algorithm Noisy data

来源：评论

学校读者我要写书评

暂无评论

Analysis of credal-c4.5 for classification in noisy domains

引用

EXPERT SYSTEMS WITH APPLIcATIONS 2016年 61卷 314-326页

作者： Mantas, carlos J. Abellan, Joaquin castellano, Javier G. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

credal Decision Trees (cDTs) are algorithms to design classifiers based on imprecise probabilities and uncertainty based information measures. These algorithms are suitable when noisy data sets are classified. This fact is explained in this paper in terms of the split criterion used in the new procedure of a special type of cDT, called credal-c4.5. This criterion is more robust to noise than the split criteria of the classic decision trees. On the other hand, all the cDTs depend on a parameter s that has a direct relation with the size of the tree built. The known c4.5 and the credal-c4.5 procedures are equivalent when the value s = 0 is used. The relation between the value of this parameter s and the capacity of the credal-c4.5 to classify data sets with label noise is studied. Also, an experimental research is carried out where credal c4.5 with different values for s are compared when they classify data sets with distinct label noise levels. From this experimentation we can conclude that, if it is possible to estimate the noise level of a data set, the choice of the correct value for s is a key point in order to obtain notably better results. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： Imprecise probabilities Imprecise Dirichlet model Uncertainty measures credal decision trees c4.5 algorithm noisy data

来源：评论

学校读者我要写书评

暂无评论

Application of Decision Tree Based on c4.5 in Analysis of coal Logistics customer

Application of Decision Tree Based on C4.5 in Analysis of Co...

引用

3rd International Symposium on Intelligent Information Technology Application

作者： Duan, Fu Zhao, Zhenxing Zeng, Xiangdong Taiyuan Univ Technol Coll Comp & Software Taiyuan Peoples R China

ISBN: (纸本)9780769538594

customer resource is the lifeline of enterprise development, and the classification management and service for customer are very important in customer relationship management (cRM). Decision tree is one of the most popular methods for classification. In this paper, we build a decision tree based on c4.5 algorithm for coal logistics customer analysis, adopt Pessimistic Error Pruning (PEP) to simplify it, and apply the rules extracted to the cRM of the coal logistics road company which shows that the method can be able to accurately classify the types of customers.

关键词： decision tree c4.5 algorithm prune customer analysis

来源：评论

学校读者我要写书评

暂无评论

Implementation of c4.5 decision tree in Human Gesture Recognition based on Doppler radars

Implementation of C4.5 decision tree in Human Gesture Recogn...

引用

International Symposium on Antennas and Propagation (ISAP)

作者： Zhang, Guiyuan Zhang, Kang Yun, Yihan Lu, Gang Lan, Shengchang Harbin Inst Technol Dept Microwave Engn Harbin Peoples R China

ISBN: (纸本)9787900914040

In order to improve the accuracy of gesture recognition, we investigate the feasibility of using a three-dimensional Doppler-radar array at 24GHz to recognize human gestures with a model consisted of ten classical gestures. On the basis of c4.5 algorithm, phase difference and spectral energy, extracted by correlation processing and power integral, are used as the features to construct decision tree and separate ten gestures. The experiment result shows that this system could achieve a high accuracy of classification reached 99.25%.

关键词： gesture recognition doppler radar array 24GHz radar decision tree c4.5 algorithm

来源：评论

学校读者我要写书评

暂无评论

An adaptive method for selecting question pools using c4.5

An adaptive method for selecting question pools using C4.5

引用

International conference on Advanced Learning Technologies

作者： Seet, Ahmad Mustafa Zualkernan, Imran A. Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9780769540559

A number of adaptive mechanisms for asking questions have been proposed. This paper presents a novel adaptive method for selecting questions from various pools of questions. The method uses a state-diagram to represent various question pools where transitions between the states are dynamically modified to generate the optimal pool of questions for the next question. c4.5 is used to close the loop on adaptive selection of questions. A case study showing how the method was implemented for an online environmental game is presented and evaluated. © 2010 IEEE.

关键词： c4.5 c4.5 algorithm adaptive mechanisms adaptive testing computer aided instruction computer games decision trees online environmental game question pool selection selecting questions state diagram

来源：评论

学校读者我要写书评

暂无评论

Damage prediction for regular reinforced concrete buildings using the decision tree algorithm

引用

cOMPUTERS & STRUcTURES 2014年 130卷 46-56页

作者： Karbassi, A. Mohebi, B. Rezaee, S. Lestuzzi, P. Ecole Polytech Fed Lausanne Appl Comp & Mech Lab CH-1015 Lausanne Switzerland Imam Khomeini Int Univ Qazvin Qazvin Iran Blekinge Tekn Hogskola Karlskrona Sweden

To overcome the problem of outlier data in the regression analysis for numerical-based damage spectra, the c4.5 decision tree learning algorithm is used to predict damage in reinforced concrete buildings in future earthquake scenarios. Reinforced concrete buildings are modelled as single-degree-of-freedom systems and various time-history nonlinear analyses are performed to create a dataset of damage indices. Subsequently, two decision trees are trained using the qualitative interpretations of those indices. The first decision tree determines whether damage occurs in an Rc building. consequently, the second decision tree predicts the severity of damage as repairable, beyond repair, or collapse. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Damage prediction Decision tree Damage index Reinforced concrete c4.5 algorithm

来源：评论

学校读者我要写书评

暂无评论

comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

引用

MEcHANIcAL SYSTEMS AND SIGNAL PROcESSING 2010年第6期24卷 1887-1906页

作者： Sakthivel, N. R. Sugumaran, V. Nair, Binoy. B. Amrita Sch Engn Dept Mech Engn Coimbatore 641105 Tamil Nadu India SRM Univ Dept Mechatron Engn Madras 603203 Tamil Nadu India Amrita Sch Engn Dept Elect & Commun Engn Coimbatore 641105 Tamil Nadu India

Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Mono-block centrifugal pump c4.5 algorithm Fault diagnosis Statistical features Rough set Fuzzy logic Principle component analysis

来源：评论

学校读者我要写书评

暂无评论

Hierarchical fuzzy clustering decision tree for classifying recipes of ion implanter

引用

EXPERT SYSTEMS WITH APPLIcATIONS 2011年第1期38卷 933-940页

作者： Horng, Shih-cheng Yang, Feng-Yi Lin, Shieh-Shing Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 41349 Taiwan Natl Yang Ming Univ Dept Biomed Imaging & Radiol Sci Taipei Taiwan St Johns Univ Dept Elect Engn Taipei Taiwan

In this paper, we propose a hierarchical fuzzy clustering decision tree (HFcDT) for the classification problem with large number of classes and continuous attributes. The HFcDT combines a division-degree matrix based hierarchal clustering technique with the entropy-based c4.5 decision tree algorithm. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. The hierarchical clustering technique splits the data set into leaf clusters using splitting attributes based on a division-degree matrix and fuzzy rules. The leaf clusters consisting of the data of more than one class will be further classified using the c4.5 algorithm. We have successfully applied the HFcDT for classifying recipes of the working wafers in an ion implanter, and compared the classification results and the training time with the existing software See5 and cART. The comparison results show that the HFcDT not only performs better than See5 and cART in the aspect of 10-fold cross validation for the average of total classification error rates but also consumes less training time. Thus, HFcDT obtains a very successful classification result. This also demonstrates why the hierarchical clustering technique helps reduce the computational complexity of the c4.5 algorithm. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： classification Fuzzy clustering Decision tree c4.5 algorithm Ion implanter

来源：评论

学校读者我要写书评

暂无评论

Analog circuit fault diagnosis based on Quantum clustering based Multi-valued Quantum Fuzzification Decision Tree (Qc-MQFDT)

引用

MEASUREMENT 2016年 93卷 421-434页

作者： cui, Yiqian Shi, Junyou Wang, Zili Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Beihang Univ Sci & Technol Key Lab Reliabil & Environm Engn Beijing Peoples R China

Analog circuit fault diagnosis is challenging due to the parametric deviation and the difficulty in signal quantizing. There still lacks effective approaches to provide reliable fault detection and classification results for a comprehensive diagnosis. In this paper, we propose a fault diagnosis methodology based on a new classification model called Quantum clustering based Multi-valued Quantum Fuzzification Decision Tree (Qc-MQFDT). Qc-MQFDT incorporates the adaptive fuzzification method to discretize continuous-valued data. The fuzzification mechanism is devised by incorporating quantum clustering (Qc) as well as the quantum membership function (QMF), where the former has the ability to sense the internal dependencies of data, and the latter uses the number of energy levels to approximate the optimal shape for fuzzy membership functions. The QF-c4.5 algorithm is developed as the decision tree learning algorithm, which employs quantum fuzzy entropy (QFE) to evaluate the information in the target variable space. The proposed method is validated using both simulated data and the real time data for the application studies of two benchmark analog circuits. The classification performances are discussed and the diagnostic capability of the model is verified through the application studies. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： Decision tree Quantum clustering (Qc) Quantum membership function (QMF) c4.5 algorithm Quantum fuzzy entropy (QFE)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：