CLASSIFICATIONS OF CREDIT CARDHOLDER BEHAVIOR BY USING FUZZY LINEAR PROGRAMMING

2004 ◽  
Vol 03 (04) ◽  
pp. 633-650 ◽  
Author(s):  
JING HE ◽  
XIANTAO LIU ◽  
YONG SHI ◽  
WEIXUAN XU ◽  
NIAN YAN
Keyword(s):  
Linear Programming ◽  
Portfolio Management ◽  
Credit Card ◽  
Real Life ◽  
Research Direction ◽  
Fuzzy Linear Programming ◽  
Research Topics ◽  
Score System ◽  
Main Research ◽  
Heuristic Classification

Behavior analysis of credit cardholders is one of the main research topics in credit card portfolio management. Usually, the cardholder's behavior, especially bankruptcy, is measured by a score of aggregate attributes that describe cardholder's spending history. In real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple linear programming-based classification methods have been promoted for analyzing credit cardholders' behaviors. As a continuation of this research direction, this paper proposes a heuristic classification method by using the fuzzy linear programming (FLP) to discover the bankruptcy patterns of credit cardholders. Instead of identifying a compromise solution for the separation of credit cardholder behaviors, this approach classifies the credit cardholder behaviors by seeking a fuzzy (satisfying) solution obtained from a fuzzy linear program. In this paper, a real-life credit database from a major US bank is used for empirical study which is compared with the results of known multiple linear programming approaches.

DATA MINING VIA MULTIPLE CRITERIA LINEAR PROGRAMMING: APPLICATIONS IN CREDIT CARD PORTFOLIO MANAGEMENT

2002 ◽  
Vol 01 (01) ◽  
pp. 131-151 ◽  
Author(s):  
YONG SHI ◽  
YI PENG ◽  
WEIXUAN XU ◽  
XIAOWO TANG
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Portfolio Management ◽  
Credit Card ◽  
Real Life ◽  
Multiple Criteria ◽  
Business Decisions ◽  
Business World ◽  
Linear Discriminant ◽  
Technology Tool

Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions. This paper provides a promising approach of data mining to classify the credit cardholders' behavior through multiple criteria linear programming. After reviewing the history of linear discriminant analyses, we will describe first a model for classifying two-group (e.g. bad or good) credit cardholder behaviors, and then a three-group (e.g. bad, normal, or good) credit model. Besides the discussion of the modeling structure, we will utilize the well-known commercial software package SAS to implement this technology by using a real-life credit card data warehouse. A number of potential business and financial applications will be finally summarized.

scholarly journals The Knowledge of Expert Opinion in Intuitionistic Fuzzy Linear Programming Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
A. Nagoorgani ◽  
J. Kavikumar ◽  
K. Ponnalagu
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Real Life ◽  
Fuzzy Linear Programming ◽  
Limited Resources ◽  
Fuzzy Data ◽  
Intuitionistic Fuzzy ◽  
Competing Demands ◽  
Intuitionistic Fuzzy Data

In real life, information available for certain situations is vague and such uncertainty is unavoidable. One possible solution is to consider the knowledge of experts on the parameters involved as intuitionistic fuzzy data. We examine a linear programming problem in which all the coefficients are intuitionistic in nature. An approach is presented to solve an intuitionistic fuzzy linear programming problem. In this proposed approach, a procedure for allocating limited resources effectively among competing demands is developed. An example is given to highlight the illustrated study.

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

2008 ◽  
pp. 26-49 ◽  
Author(s):  
Yong Shi ◽  
Yi Peng ◽  
Gang Kou ◽  
Zhengxin Chen
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Credit Card ◽  
Programming Model ◽  
Real Life ◽  
Multiple Criteria ◽  
Linear Programming Model ◽  
Multiple Criteria Optimization ◽  
Research Challenges ◽  
Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment

2020 ◽  
Vol 9 (2) ◽  
pp. 132-161 ◽  
Author(s):  
Ranjan Kumar ◽  
Sripati Jha ◽  
Ramayan Singh
Keyword(s):  
Linear Programming ◽  
Shortest Path ◽  
Path Length ◽  
Real Life ◽  
Shortest Path Problem ◽  
Fuzzy Linear Programming ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
Fuzzy Environment

The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat fuzzy membership functions. Moreover, the authors can solve the fuzzy shortest path problem (FSPP) with two different membership functions such as normal and a fuzzy membership function under real-life situations. The transformation of the fuzzy linear programming (FLP) model into a crisp linear programming model by using a score function is also investigated. Furthermore, the shortcomings of some existing methods are discussed and compared with the algorithm. The objective of the proposed method is to find the fuzzy shortest path (FSP) for the given network; however, this is also capable of predicting the fuzzy shortest path length (FSPL) and crisp shortest path length (CSPL). Finally, some numerical experiments are given to show the effectiveness and robustness of the new model. Numerical results show that this method is superior to the existing methods.

Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

2011 ◽  
Vol 7 (3) ◽  
pp. 88-101 ◽  
Author(s):  
DongHong Sun ◽  
Li Liu ◽  
Peng Zhang ◽  
Xingquan Zhu ◽  
Yong Shi
Keyword(s):  
Linear Programming ◽  
Portfolio Management ◽  
Real World ◽  
Credit Card ◽  
Extraction Method ◽  
Large Scale ◽  
Programming Model ◽  
Rule Extraction ◽  
Multiple Criteria ◽  
Data Sets

Due to the flexibility of multi-criteria optimization, Regularized Multiple Criteria Linear Programming (RMCLP) has received attention in decision support systems. Numerous theoretical and empirical studies have demonstrated that RMCLP is effective and efficient in classifying large scale data sets. However, a possible limitation of RMCLP is poor interpretability and low comprehensibility for end users and experts. This deficiency has limited RMCLP’s use in many real-world applications where both accuracy and transparency of decision making are required, such as in Customer Relationship Management (CRM) and Credit Card Portfolio Management. In this paper, the authors present a clustering based rule extraction method to extract explainable and understandable rules from the RMCLP model. Experiments on both synthetic and real world data sets demonstrate that this rule extraction method can effectively extract explicit decision rules from RMCLP with only a small compromise in performance.

Fuzzy Linear Programming: A Modern Tool for Decision Making

2012 ◽  
Author(s):  
Pandian M. Vasant ◽  
R. Nagarajan ◽  
Sazali Yaacob
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Production Planning ◽  
Membership Function ◽  
Industrial Production ◽  
Real Life ◽  
Fuzzy Linear Programming ◽  
Programming Approach ◽  
Modern Trend ◽  
Application Problem

The modern trend in industrial application problem deserves modeling of all relevant vague or fuzzy information involved in a real decision making problem. In the first part of the paper, some explanations on tri partite fuzzy linear programming approach and its importance have been given. In the second part, the usefulness of the proposed S-curve membership function is established using a real life industrial production planning of a chocolate manufacturing unit. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. A solution to this problem establishes the usefulness of the suggested membership function for decision making in industrial production planning. Key words: Fuzzy linear programming, Satisfactory solution; Decision maker; Implementer; Analyst; Fuzzy constraint; Vagueness.

Application of Multi Objective Fuzzy Linear Programming in Supply Production Planning Problem

2012 ◽  
Author(s):  
Pandian M. Vasant
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Membership Function ◽  
Programming Model ◽  
Real Life ◽  
Fuzzy Linear Programming ◽  
Programming Approach ◽  
Planning Problem ◽  
Production Planning Problem ◽  
S Curve

The objective of this paper is to establish the usefulness of modified s-curve membership function in a limited supply production planning problem with continuous variables. In this respect, fuzzy parameters of linear programming are modeled by non-linear membership functions such as s-curve function. This paper begins with an introduction and construction of the modified s-curve membership function. A numerical real life example of supply production planning problem is then presented. The computational results show the usefulness of the modified s-curve membership function with fuzzy linear programming technique in optimising individual objective functions, compared to non-fuzzy linear programming approach. Futhermore, the optimal solution helps to conclude that by incorporating fuzziness in a linear programming model through the objective function and constraints, a better level of satisfactory solution will be provided in respect to vagueness, compared to non-fuzzy linear programming.

SOLVING FUZZY LINEAR PROGRAMMING PROBLEMS WITH MODIFIED S-CURVE MEMBERSHIP FUNCTION

2005 ◽  
Vol 13 (01) ◽  
pp. 97-109 ◽  
Author(s):  
PANDIAN M. VASANT
Keyword(s):  
Linear Programming ◽  
Membership Function ◽  
Fuzzy Numbers ◽  
Numerical Solutions ◽  
Real Life ◽  
Optimization Method ◽  
Fuzzy Linear Programming ◽  
Membership Functions ◽  
Linear Programming Problems ◽  
S Curve

In this paper, we concentrate on two kinds of fuzzy linear programming problems: linear programming problems with only fuzzy resource variables and linear programming problems in which both the resource variables and the technological coefficients are fuzzy numbers. We consider here only the case of fuzzy numbers with modified s-curve membership functions. We propose here the modified s-curve membership function as a methodology for fuzzy linear programming and use it for solving these problems. We also compare the new proposed method with non-fuzzy linear programming optimization method. Finally, we provide real life application examples in production planning and their numerical solutions.

CLASSIFYING CREDIT CARD ACCOUNTS FOR BUSINESS INTELLIGENCE AND DECISION MAKING: A MULTIPLE-CRITERIA QUADRATIC PROGRAMMING APPROACH

2005 ◽  
Vol 04 (04) ◽  
pp. 581-599 ◽  
Author(s):  
YONG SHI ◽  
YI PENG ◽  
GANG KOU ◽  
ZHENGXIN CHEN
Keyword(s):  
Decision Making ◽  
Quadratic Programming ◽  
Portfolio Management ◽  
Business Intelligence ◽  
Credit Card ◽  
Real Life ◽  
Multiple Criteria ◽  
Programming Approach ◽  
Support Vector ◽  
Linear Discriminant

A major challenge in credit card portfolio management is to classify and predict credit cardholders' behaviors in a reliable precision because cardholders' behaviors are rather dynamic in nature. This is crucial for creditors because it allows them to take proactive actions and minimize charge-off and bankruptcy losses. Although the methods used in the area of credit portfolio management have improved significantly, the demand for alternative and sophisticated analytical tools is still strong. The objective of this paper is to propose a multiple criteria quadratic programming (MCQP) to classify credit card accounts for business intelligence and decision making. MCQP is intended to predict credit cardholders' behaviors from a nonlinear perspective that is justifiable because both the objective functions and constraints in credit card accounts classification may be nonlinear. Using a real-life credit card dataset from a major US bank, the MCQP method is compared with popular and similar classification methods: linear discriminant analysis, decision tree, multiple criteria linear programming, support vector machine, and neural network. The results indicate that MCQP is a promising business intelligence method in credit card portfolio management.

A FUZZY LINEAR PROGRAMMING-BASED CLASSIFICATION METHOD

2011 ◽  
Vol 10 (06) ◽  
pp. 1161-1174 ◽  
Author(s):  
AIHUA LI ◽  
YONG SHI ◽  
JING HE ◽  
YANCHUN ZHANG
Keyword(s):  
Linear Programming ◽  
Financial Risk ◽  
General Framework ◽  
Risk Control ◽  
Real Life ◽  
Multiple Criteria ◽  
Fuzzy Linear Programming ◽  
The Other ◽  
Classification Method ◽  
Classification Models

Multiple criteria linear programming and multiple criteria quadratic programming classification models have been applied in some field in financial risk analysis and credit risk control such as credit cardholders' behavior analysis. In this paper, a fuzzy linear programming classification method with soft constraints and criteria was proposed based on the previous findings from other researchers. In this method, the satisfied result can be obtained through selecting constraint and criteria boundary variable di*, respectively. A general framework of this method is also constructed. Two real-life datasets, one from a major USA bank and the other from a database of KDD 99, are used to test the accurate rate of the proposed method. And the result shows the feasibility of this method.

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