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Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

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Date Issued:
2014
Abstract/Description:
Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
Title: Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications.
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Name(s): Razzaghi, Talayeh, Author
Xanthopoulos, Petros, Committee Chair
Karwowski, Waldemar, Committee Member
Pazour, Jennifer, Committee Member
Mikusinski, Piotr, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2014
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
Identifier: CFE0005542 (IID), ucf:50298 (fedora)
Note(s): 2014-05-01
Ph.D.
Engineering and Computer Science, Industrial Engineering and Management Systems
Doctoral
This record was generated from author submitted information.
Subject(s): Classification -- Imbalanced data -- Cost-sensitive learning -- Outliers -- Weighted support vector machine -- Relaxed support vector machines -- Control chart pattern recognition
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005542
Restrictions on Access: public 2014-11-15
Host Institution: UCF

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