局部学习半监督多类分类机  

Local learning semi-supervised multi-class classifier

作　　者：吕佳[1,2,3] 邓乃扬[3] 田英杰[4] 邵元海[5] 杨新民[6] 

机构地区：[1]内蒙古大学数学科学学院,呼和浩特010021 [2]重庆师范大学计算机与信息科学学院,重庆400047 [3]中国农业大学理学院,北京100083 [4]中国科学院虚拟经济与数据科学研究中心,北京100190 [5]浙江工业大学之江学院,杭州310024 [6]重庆师范大学数学学院,重庆400047

出　　处：《系统工程理论与实践》2013年第3期748-754,共7页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金重点项目(10831009);国家自然科学基金(10971223;11071252;70921061);重庆市教委科技项目(KJ120628);重庆师范大学博士启动基金(12XLB030)

摘　　要：半监督多类分类问题是机器学习和模式识别领域中的一个研究热点,目前大多数多类分类算法是将问题分解成若干个二类分类问题来求解.提出两种类标号表示方法来避免多个二类分类问题的求解,一种是单位圆类标号表示方法,一种是二进制序列类标号表示方法,并利用局部学习在二类分类问题中的良好学习特性,提出基于局部学习的半监督多类分类机.实验结果证明采用了基于局部学习的半监督多类分类机错分率更小,稳定性更高.Semi-supervised multi-class classification problem opens research focuses in machine learning and pattern recognition, currently it is decomposed into a set of binary classification problems. Two kinds of class label presentation methods that one was class label presentation method of unit disc and the other was that of binary string were proposed for fear that multiple binary classification problems were solved. Besides, local learning has the good feature in semi-supervised binary classification problem. On the basis of it, local learning semi-supervised multi-class classifier was presented in this paper. The effectiveness of the algorithms was confirmed with experiments on benchmark datasets compared to other related algorithms.

关 键 词：半监督分类 多类分类机 局部学习 二进制序列 单位圆 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]