A Classification Model for a Two-Class (New Product Purchase) Discrimination Process Using Multiple-Criteria Linear Programming

2007 ◽  
pp. 295-304
Author(s):  
Dinesh Pai ◽  
Sheila Lawrence
Keyword(s):  
Linear Programming ◽  
New Product ◽  
Multiple Criteria ◽  
Classification Model

scholarly journals Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

2011 ◽  
Vol 27 (5) ◽  
pp. 73 ◽  
Author(s):  
Wikil Kwak ◽  
Susan Eldridge ◽  
Yong Shi ◽  
Gang Kou
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Financial Distress ◽  
Multiple Criteria ◽  
Financial Statement ◽  
Font Size ◽  
Explanatory Variables ◽  
Data Mining Approach ◽  
Starting Point ◽  
Accuracy Rates

<span style="font-family: Times New Roman; font-size: small;"> </span><h1 style="margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;"><span style="font-family: Times New Roman;"><span style="color: black; font-size: 10pt; mso-themecolor: text1;">Our study evaluates a multiple criteria linear programming (MCLP) </span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">and other </span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">data mining approach</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">es</span><span style="color: black; font-size: 10pt; mso-themecolor: text1;"> </span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">to predict auditor changes using a portfolio of financial statement measures to capture financial distress</span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">.<span style="mso-spacerun: yes;"> </span>The results of the MCLP approach and the other data mining approaches show that these methods perform</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;"> reasonably well to predict auditor changes </span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">using financial distress variables.</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;"><span style="mso-spacerun: yes;"> </span>Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent.<span style="mso-spacerun: yes;"> </span>Our study is designed to establish a starting point for auditor-change prediction using financial distress variables.<span style="mso-spacerun: yes;"> </span>Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</span></span></h1><span style="font-family: Times New Roman; font-size: small;"> </span>

scholarly journals MODEL ABC MULTI-CRITERIA INVENTORY CLASSIFICATION (MCIC) MENGGUNAKAN PEMROGRAMAN LINEAR PADA PRODUK KIMIA

2019 ◽  
Vol 3 (2) ◽  
pp. 106-118
Author(s):  
Eko Pratomo (Politeknik APP - Indonesia)
Keyword(s):  
Linear Programming ◽  
Lead Time ◽  
Multiple Criteria ◽  
Traditional Model ◽  
Abc Model ◽  
Linear Programming Method ◽  
Inventory Policies ◽  
Chemical Product ◽  
Class A ◽  
Class B

Abstract In order to maintain their inventory efficiently, enterprises need to prioritize inventory policies considering multiple criteria. A Multi Criteria Inventory Classification (MCIC) is one of the most effective techniques widely used to classify inventory. In this paper, multiple criteria (annual value, lead time, cost per unit) are considered on ABC inventory classification. The aim of this study is classify products considering those multiple criteria. Multiple criteria ABC Classifications methodology developed by Ramanathan-Model and Ng-Model are used and compared with traditional method. Data are collected from annual chemical product transaction on PT XYZ during 2018. In this paper, linear programming method is used to solve ABC MCIC Model. The result of this study show that 12 items (14%) are identified as Class A, 26 items (30%) as class B and the remaining 48 items (56%) as C Class. In our conclution, we propose inventory policies based on the result of the ABC Models. Keywords: ABC Model; MCIC; Traditional Model; Ramanathan-Model; Ng-Model; Linear Programming; Chemical Product.Abstrak Dalam mengelola persediaan secara efisien, perusahaan perlu menentukan prioritas pengelolaan persediaan dengan mempertimbangkan beberapa kriteria. Klasifikasi ABC Multi Kriteria (MCIC) merupakan model klasifikasi persediaan barang yang umum digunakan oleh perusahaan dalam mengelola persediaan dalam jumlah besar. Penelitian ini menggunakan multi kriteria berupa nilai total produk, lead time dan biaya per unit. Tujuan penelitian adalah mengelompokan jenis/kelas barang sesuai dengan tingkat kepentingan dengan mempertimbangkan multi kriteria.  Metode Multi kriteria yang telah dikembangkan oleh Ramanathan-Model dan Ng-Model dibandingkan dengan hasil klasifikasi Single criteria ABC (Traditional model). Data yang digunakan adalah data tahunan transaksi produk kimia PT XYZ di tahun 2018. Penyelesaian model ABC multi kriteria (MCIC) dengan pemrograman linear. Terdapat 86 items produk kimia yang diklasifikasikan dengan hasil klasifikasi A sejumlah 12 item (14%), B sejumlah 26 item (30%) dan item C sejumlah 48 item (56%). Pada penelitan ini juga disampaikan kebijakan inventory masing-masing kelas berdasarkan hasil klasifikasi ABC model yang telah dilakukan.Kata Kunci: Model ABC; MCIC; Model tradisional; Model Ramanathan; Model Ng; Pemrograman Linear; Produk kimia.

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.

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.

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