一种融合本体和上下文的自适应层次分类模型  

An Adaptive Hierarchical Model Based on Fusion of Ontology and Context

作　　者：古平[1] 朱庆生[1] 张程[1] 庄致[2] 

机构地区：[1]重庆大学计算机学院,重庆400044 [2]重庆交通大学管理学院,重庆400074

出　　处：《北京理工大学学报》2009年第10期885-889,共5页Transactions of Beijing Institute of Technology

基　　金：国家自然科学基金资助项目(604003009);重庆市自然科学基金资助项目(2005BB2224)

摘　　要：提出一种新的自适应层次分类(HAC)模型,通过本体对特征集进行语义扩展,并以增量形式在层次模型中构建特征上下文和类别相关上下文,以辅助实现一种高效、无阻滞的层次分类.实验结果证明,模型HAC具有相对更好的分类性能,而且分类速度更快,有利于实现实时在线的文档分类.In documents classification,utilization of ontology and context is proven an effective way to improve text classification,but also presented as a difficult problem.This paper presents a novel adaptive hierarchical classification model（HAC） which is based on concept expansion with ontology.Using the hierarchy structure of the model,an incremental learning algorithm in terms of both feature and category associated context is presented,and then with these,a more efficient and blocking-immuned hierarchical classification method is proposed.Experimental results showed that better performance can be achieved with less classification time in HAC,which is of particular significance in document online classification.

关 键 词：增量学习 上下文 本体 层次分类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]