.png){kind=icon}
返回
Data-driven decomposition for multi-class classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2007.05.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explore the distribution of data classes and optimize both the composition and the number of base learners to design an effective and compact code matrix. Two real world applications are studied: (1) the holistic recognition (i.e., recognition without segmentation) of touching handwritten numeral pairs and (2) the classification of cancer tissue types based on microarray gene expression data. The results show that the proposed DECOC is able to deliver competitive accuracy compared with other ECOC methods, using parsimonious base learners than the pairwise coupling (one-vs-one) decomposition scheme. With a rejection scheme defined by a simple robustness measure, high reliabilities of around 98% are achieved in both applications. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
multi-class classification
error correcting output coding (ECOC)
data-driven error correcting output coding (DECOC)
support vector machine
handwritten numeral recognition
gene expression classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
Synthesis and structure-activity studies of SH2 binding peptides containing hydrolytically stable analogs of O -phosphotyrosine.png)
Peptides
PeptidesIF0
PREAI
分享收藏Verification - a method of enhancing the recognizers of isolated and touching handwritten numeralsPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容