Outliers in statistical pattern recognition and an application to automatic chromosome classification

作     者：Ritter, G Gallegos, MT 

作者机构：Fakultät für Mathematik und Informatik Universität Passau D-94 030 Passau Germany 

出 版 物：《PATTERN RECOGNITION LETTERS》 (模式识别快报)

年 卷 期：1997年第18卷第6期

页      面：525-539页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：outlier estimation mixture distributions trimming method Bayesian classification statistical pattern recognition automatic chromosome classification karyotyping diagnostic classification biomedical data model 

摘      要：We propose a heuristic method of parameter estimation in mixture models for data with outliers and design a Bayesian classifier for assignment of m objects to n greater than or equal to m classes under constraints. This method of outlier handling combined with the classifier is applied to the well-known problem of automatic, constrained classification of chromosomes into their biological classes. We show that it decreases the error rate relative to the classical, normal, model by more than 50%. When applied to the Edinburgh feature data of the large Copenhagen image data set Cpr our best classifier yields an error rate close to 1.3% relative to chromosomes;4 out of 5 cells are correctly classified. (C) 1997 Elsevier Science B.V.