Data Classification via a New Data Mining Approach: Multiple Criteria Programming with Multiple Kernels

2009 ◽  
Author(s):  
Wenzhou Wang ◽  
Liwei Wei
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Multiple Criteria ◽  
Data Mining Approach ◽  
Multiple Criteria Programming

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>

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction
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