数据采集与处理2018，Vol.33Issue(2)：359-369,11.DOI:10.16337/j.1004-9037.2018.02.019
多示例学习的示例层次覆盖算法
Multi-instance Learning with Instance-Level Covering Algorithm
摘要
Abstract
In multi-instance learning,the core instances play an important role on the prediction of bags' label.And if two instances have different numbers of instances with the same category around them,they have different levels of representative.In order to improve the classification accuracy,multi-instance learning with instance-level covering algorithm (MILICA) is proposed by which we could select the most representative instances to form the core instance set.Firstly,with the max Hausdorff distance and the covering algorithm,the initial core instance set is constructed.Then,the final core instance set and the number of instances in a cover are obtained.Finally,a similarity measure function is used to convert a bag into a single sample for classification.Experimental results on two-category datasets and multi-category image datasets demonstrate that the proposed MILICA method has perfect classification capability.关键词
多示例学习/覆盖算法/核心示例集/相似度函数Key words
multi-instance learning/covering algorithm/core instance set/similarity measure function分类
信息技术与安全科学引用本文复制引用
董露露,谢飞,章程..多示例学习的示例层次覆盖算法[J].数据采集与处理,2018,33(2):359-369,11.基金项目
国家自然科学基金(61503116)资助项目 （61503116）
安徽省教育厅自然科学基金重点(KJ2014A081)资助项目 （KJ2014A081）
安徽省级优秀青年基金重点(2013SQRL097ZD)资助项目 （2013SQRL097ZD）
安徽省自然科学基金(1408085QF108)资助项目. （1408085QF108）
