MULTI-CLASS DIAGNOSIS CLASSIFICATION ON HIGH DIMENSION DATA BY LOGISTIC MODELS

摘要：

Logistic regression has been increasingly used in chronic gastritis research. Using expression of logistic regression monitored simultaneously by Maximum likelihood estimation, contribution of gastritis symptom to the syndrome classifications are distinguished, and chronic gastritis samples are more accurately classified. While Logistic regression has been extensively evaluated for dichotomous classification, there are only limited reports on the important issue of multi-class chronic gastritis classification. It needs to explore the logistic regression of the multi-class chronic gastritis classification.In this research, we address multi-class chronic gastritis classification by applying Logistic regression based methods on data of nominal and ordinal scaled sample class outcomes, e.g., samples of different chronic gastritis subtypes. Logistic regression based classifiers are assessed by accurate classification rates on chronic gastritis data and comparing with HGC model discrimination based classifiers. The result shows that classify performance derive from Logistic regression model has the advantage over traditional model by 26.94％.

关键词： Multi-class classifier Logistic Regression Mazimum likelihood estimation Chronic gastritis

作者: TONG-SHENG CHEN XUE-QIN HU SHAO-ZI LI CHANG-LE ZHOU

作者单位: Department of Intelligence Science and Technology, Xiamen University, Xiamen 361005, China Departmen College of Basic medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, Chi Department of Intelligence Science and Technology, Xiamen University, Xiamen 361005, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3301-3306

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）