检索结果-南通市图书馆

Information entropy for ordinal classification

Science China(Information Sciences) 2010年第6期53卷 1188-1200页

作者： HU QingHua , GUO MaoZu, YU DaRen & LIU JinFu Harbin Institute of Technology, Harbin 150001, China Harbin Institute of Technology Harbin China

ordinal classification plays an important role in various decision making ***, little attention is paid to this type of learning tasks compared with general classification *** information entropy and the derived measure of mutual information play a fundamental role in a number of learning algorithms including feature evaluation, selection and decision tree *** measures are not applicable to ordinal classification for they cannot characterize the consistency of monotonicity in ordinal *** this paper, we generalize Shannon’s entropy to crisp ordinal classification and fuzzy ordinal classification, and show the information measures of ranking mutual information and fuzzy ranking mutual *** discuss the properties of these measures and show that the proposed ranking mutual information and fuzzy ranking mutual information are the indexes of consistency of monotonicity in ordinal *** addition, the proposed indexes are used to evaluate the monotonicity degree between features and decision in the context of ordinal classification.

关键词： ordinal classification information entropy ranking entropy ranking mutual information

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Apple grading method based on neural network with ordered partitions and evidential ensemble learning

引用

CAAI Transactions on Intelligence Technology 2022年第4期7卷 561-569页

作者： Liyao Ma Peng Wei Xinhua Qu Shuhui Bi Yuan Zhou Tao Shen School of Electrical Engineering University of JinanJinanChina Blekinge Institute of Technology KarlskronaSweden

In order to improve the performance of the automatic apple grading and sorting system,in this paper,an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is *** a non-destructive grading method,apples are graded into three grades based on the Soluble Solids Content value,with features extracted from the preprocessed near-infrared spectrum of apple serving as model *** the uncertainty in grading labels,mass generation approach and evidential encoding scheme for ordinal label are proposed,with uncertainty handled within the framework of Dempster–Shafer *** neural network with ordered partitions as the base learner,the learning procedure of the Bagging-based ensemble model is *** on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

关键词： apple grading Demspter-Shafer theory ensemble learning ordinal classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Dominance-based rough set approach as a paradigm of knowledge discovery and granular computing

引用

重庆邮电大学学报（自然科学版） 2010年第6期22卷 708-719页

作者： Roman Slowinski Institute of Computing Science Poznan University of Technology60-965 Poznan Systems Research Institute Polish Academy of Sciences

Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or *** has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered *** computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.

关键词： rough sets dominance-based rough set approach(DRSA) ordinal classification variable-consistency DRSA monotonic decision rules granular computing

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

ordinal Decision Trees

引用

浙江海洋学院学报（自然科学版） 2010年第5期29卷 450-461页

作者： HU Qinghua CHE Xunjian School of Energy Science and Engineering Harbin Institute of Technology Harbin Heilongjiang 150001 P. R. China

In many decision making tasks,the features and decision are *** ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world *** this work,we propose a new measure of feature quality, called rank mutual ***,we design an ordinal decision tree(REOT) construction technique based on rank mutual *** theoretic and experimental analysis shows that the proposed algorithm is effective.

关键词： ordinal classification rank entropy rank mutual information decision tree

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：