Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography

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Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography

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作者：Jae Young Choi^a ; ¹ ; ^{jygchoi@kaist.ac.kr" class="auth_mail" title="E-mail the corresponding author} ; ^{jyoung.choi@jwu.ac.kr" class="auth_mail" title="E-mail the corresponding author} ; Dae Hoe Kim^a ; ^{dhkim10@kaist.ac.kr" class="auth_mail" title="E-mail the corresponding author} ; Konstantinos N. Plataniotis^b ; ^{kostas@ece.utoronto.ca" class="auth_mail" title="E-mail the corresponding author} ; Yong Man Ro^a ; ^{ymro@ee.kaist.ac.kr" class="auth_mail" title="E-mail the corresponding author}
关键词：Ensemble learning ; Ensemble selection ; Classification ; Mammographic masses ; Multiple feature representations ; Computer-aided Detection (CADe) ; Computer-aided Diagnosis (CADx)
刊名：Expert Systems with Applications
出版年：2016
出版时间：15 March 2016
年：2016
卷：46
期：Complete
页码：106-121
全文大小：2492 K

文摘

class="label">•: Novel ensemble classifier framework for improved classification of breast lesions.
class="label">•: Ensemble generation algorithm using different types of breast lesion features.
class="label">•: Ensemble selection mechanism to find an optimal subset of component classifiers.
class="label">•: Impressive classification performance by comparing single classifier based methods.