Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

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.

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

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch004 ◽

2008 ◽

pp. 26-49 ◽

Cited By ~ 1

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.

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

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622002000038 ◽

2002 ◽

Vol 01 (01) ◽

pp. 131-151 ◽

Cited By ~ 91

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.

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

Research and Trends in Data Mining Technologies and Applications ◽

10.4018/978-1-59904-271-8.ch009 ◽

2007 ◽

pp. 242-275 ◽

Cited By ~ 2

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.

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Solution of the mixed integer large scale unit commitment problem by means of a continuous Stochastic linear programming model

Energy Systems ◽

10.1007/s12667-013-0107-z ◽

2013 ◽

Vol 5 (2) ◽

pp. 269-284 ◽

Cited By ~ 10

Author(s):

D. Siface ◽

M. T. Vespucci ◽

A. Gelmini

Keyword(s):

Linear Programming ◽

Large Scale ◽

Unit Commitment ◽

Programming Model ◽

Linear Programming Model ◽

Mixed Integer ◽

Stochastic Linear Programming ◽

Unit Commitment Problem ◽

Scale Unit ◽

Commitment Problem

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

Data Mining Applications for Empowering Knowledge Societies ◽

10.4018/978-1-59904-657-0.ch001 ◽

2009 ◽

pp. 1-25

Author(s):

Yong Shi ◽

Yi Peng ◽

Gang Kou ◽

Zhengxin Chen

Keyword(s):

Data Mining ◽

Linear Programming ◽

Programming Model ◽

Neuronal Damage ◽

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 damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

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Multiple criteria network models for project management

Agricultural Economics (Zemědělská ekonomika) ◽

10.17221/5169-agricecon ◽

2012 ◽

Vol 50 (No. 2) ◽

pp. 71-76 ◽

Cited By ~ 1

Author(s):

T. Šubrt

Keyword(s):

Linear Programming ◽

Project Management ◽

Programming Model ◽

Critical Path ◽

Network Models ◽

Multiple Criteria ◽

Linear Programming Model ◽

Software Support ◽

Task Duration ◽

Starting Point

The aim of the paper is to present one possibility of how to model and solve a resource oriented critical path problem. As a starting point, a single criteria model for critical path finding is shortly mentioned. Lately, more criteria functions for this model are defined. If any project task uses more resources for its completion, its duration usually depends on only one of them – other resources are not fully used. In here defined multiple criteria approach, these dependencies are not assumed. Each criteria function is derived from a theoretical task duration based on a number of units of only one resource and on its importance. Using either linear programming model with aggregated criteria function or simple Excel calculation with Microsoft Project software support, a so-called compromise critical path can be found. On this path, some resources are overallocated and some are underallocated but the total sum of all underallocations and all overallocations is minimized. All resources are used as effectively as possible and the project is as short as possible too.

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A Linear Programming Model for Daily Harvesting Operations at the Large-Scale Grain Farm of the IJsselmeerpolders Development Authority

Journal of the Operational Research Society ◽

10.2307/2580923 ◽

1981 ◽

Vol 32 (7) ◽

pp. 535

Author(s):

B. Fokkens ◽

M. Puylaert

Keyword(s):

Linear Programming ◽

Large Scale ◽

Programming Model ◽

Linear Programming Model

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A large-scale linear programming model for finding optimal container inspection strategies

Naval Research Logistics (NRL) ◽

10.1002/nav.20349 ◽

2009 ◽

Vol 56 (5) ◽

pp. 404-420 ◽

Cited By ~ 26

Author(s):

E. Boros ◽

L. Fedzhora ◽

P. B. Kantor ◽

K. Saeger ◽

P. Stroud

Keyword(s):

Linear Programming ◽

Large Scale ◽

Programming Model ◽

Linear Programming Model ◽

Container Inspection

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CLASSIFICATIONS OF CREDIT CARDHOLDER BEHAVIOR BY USING FUZZY LINEAR PROGRAMMING

International Journal of Information Technology & Decision Making ◽

10.1142/s021962200400129x ◽

2004 ◽

Vol 03 (04) ◽

pp. 633-650 ◽

Cited By ~ 49

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.

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