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.

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

2007 ◽  
pp. 242-275 ◽  
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.

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

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.

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.

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.

scholarly journals Multiple criteria network models for project management

2012 ◽  
Vol 50 (No. 2) ◽  
pp. 71-76 ◽  
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.

scholarly journals Z-number CCR using Trapezoidal Fuzzy Numbers

2019 ◽  
Vol 8 (11S2) ◽  
pp. 294-298
Keyword(s):  
Linear Programming ◽  
Membership Function ◽  
Programming Model ◽  
Real Life ◽  
Efficiency Score ◽  
Linear Programming Model ◽  
Measuring Efficiency ◽  
Linguistic Assessment ◽  
Ccr Model ◽  
Inputs And Outputs

Data envelopment analysis (DEA) is a powerful tool for measuring efficiency of multiple inputs and outputs of a set of decision making units (DMUs). There are several models in DEA such as the Banker, Charnes and Cooper (BCC) model, Andersen and Peterson (AP) model and many more. The data used are normally crisps but in real life, data are usually vague or imprecise such as in real problems that are characterized by linguistic information given by experts. In such cases, the Znumber has been used as it takes into account expert’s reliability on the information given. Currently, the triangular membership function is used in the Z-number CCR model. However, in linguistic assessment the trapezoidal membership function is better suited to capture the vagueness of the assessment. The Znumber CCR model using the trapezoidal membership function for inputs and outputs of a set of DMUs is proposed in this paper. In the present study, the Z-number CCR using trapezoidal membership function is converted into a linear programming model and a crisp linear programming model is obtained by employing α-cut approach,. A numerical example on portfolio selection in Information Systems/Information Technology (IS/IT) is presented to demonstrate the proposed method and to find the best portfolio by ranking them according to their efficiency score.

scholarly journals Linear programming problems with some multi-choice fuzzy parameters

2018 ◽  
Vol 28 (2) ◽  
pp. 249-264 ◽  
Author(s):  
Avik Pradhan ◽  
Biswal Prasad
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Programming Model ◽  
Real Life ◽  
Optimal Solution ◽  
Linear Programming Model ◽  
Mixed Integer ◽  
Non Linear Programming ◽  
A Value ◽  
Linear Programming Problems

In this paper, we consider some Multi-choice linear programming (MCLP) problems where the alternative values of the multi-choice parameters are fuzzy numbers. There are some real-life situations where we need to choose a value for a parameter from a set of different choices to optimize our objective, and those values of the parameters can be imprecise or fuzzy. We formulate these situations as a mathematical model by using some fuzzy numbers for the alternatives. A defuzzification method based on incentre point of a triangle has been used to find the defuzzified values of the fuzzy numbers. We determine an equivalent crisp multi-choice linear programming model. To tackle the multi-choice parameters, we use Lagranges interpolating polynomials. Then, we establish a transformed mixed integer nonlinear programming problem. By solving the transformed non-linear programming model, we obtain the optimal solution for the original problem. Finally, two numerical examples are presented to demonstrate the proposed model and methodology.

Intuitionistic fuzzy multi-objective milk-run modelling under time window constraints

2021 ◽  
pp. 1-16
Author(s):  
Esra Çakir ◽  
Ziya Ulukan ◽  
Cengiz Kahraman ◽  
Canan Ölçer Sağlam ◽  
Burcu Kuleli Pak ◽  
...  
Keyword(s):  
Linear Programming ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Transportation Cost ◽  
Linear Programming Model ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Time Window Constraints ◽  
Milk Run

Milk-run is a delivery method allowing to move small quantities of a large number of different items with predictable lead times from various suppliers to a customer. The main goal is to minimize the transportation cost by minimizing the travel distance and by maximizing vehicle capacities. The effects of uncertainties in arrival times of vehicles and loading times of shipments should also be considered in modeling the milk-run problems. In this paper, a multi-objective linear programming model, an ordinary fuzzy multi-objective linear programming model and an intuitionistic fuzzy multi-objective linear programming model are proposed for the milk-run modeling under time window constraints. The proposed approaches are applied on the real-life data of Borusan Logistics which is the one of the largest logistics firms in Turkey and the results are presented.

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