A multi-instance learning algorithm based on nonparallel classifier

被引：5

作者：

Qi, Zhiquan ^{[1
]}

Tian, Yingjie ^{[1
]}

Yu, Xiaodan ^{[2
]}

Shi, Yong ^{[1
]}

机构：

[1] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China

[2] Univ Int Business & Econ, Beijing 100029, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 241卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Data mining; Multi-instance learning; SVM; Machine learning; Nonparallel classifier; SUPPORT VECTOR MACHINE; FRAMEWORK;

D O I：

10.1016/j.amc.2014.05.016

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we proposed a new Multiple-Instance Learning (MIL) method based on nonparallel classifier (called MI-NSVM). The method is mainly divided into two steps. The first step is to generate a spare hyperplane and estimate the score of each instance in positive bags. For the second step, MI-NSVM seeks the "most positive" instance of each positive bag by the information obtained in the first step, and then generates the second hyperplane. MI-NSVM is a useful extension of twin SVM and has the same advantages as it. All experiments show that our method is superior to the traditional MI-SVM and MI-TSVM in both computation time and classification accuracy. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：233 / 241

页数：9

共 50 条

[31] Feature selection in multi-instance learning
Gan, Rui
Yin, Jian
NEURAL COMPUTING & APPLICATIONS, 2013, 23 (3-4) : 907 - 912
[32] Adapting RBF Neural Networks to Multi-Instance Learning
Min-Ling Zhang
Zhi-Hua Zhou
Neural Processing Letters, 2006, 23 : 1 - 26
[33] Adapting RBF neural networks to multi-instance learning
Zhang, ML
Zhou, ZH
NEURAL PROCESSING LETTERS, 2006, 23 (01) : 1 - 26
[34] Generalized attention-based deep multi-instance learning
Zhao, Lu
Yuan, Liming
Hao, Kun
Wen, Xianbin
MULTIMEDIA SYSTEMS, 2023, 29 (01) : 275 - 287
[35] A multi-instance ensemble learning model based on concept lattice
Kang, Xiangping
Li, Deyu
Wang, Suge
KNOWLEDGE-BASED SYSTEMS, 2011, 24 (08) : 1203 - 1213
[36] A Prototype Learning Based Multi-Instance Convolutional Neural Network
He K.-L.
Shi Y.-H.
Gao Y.
Huo J.
Wang D.
Zhang Y.
Gao, Yang (gaoy@nju.edu.cn), 2017, Science Press (40): : 1265 - 1274
[37] Generalized attention-based deep multi-instance learning
Lu Zhao
Liming Yuan
Kun Hao
Xianbin Wen
Multimedia Systems, 2023, 29 : 275 - 287
[38] Landmark-based deep multi-instance learning for brain disease diagnosis
Liu, Mingxia
Zhang, Jun
Adeli, Ehsan
Shen, Dinggang
MEDICAL IMAGE ANALYSIS, 2018, 43 : 157 - 168
[39] MULTI-INSTANCE LEARNING VIA INSTANCE-BASED AND BAG-BASED REPRESENTATION TRANSFORMATIONS
Yuan, Liming
Zhao, Lu
Xu, Haixia
2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2015, : 2771 - 2775
[40] Instance Explainable Multi-instance Learning for ROI of Various Data
Zhao, Xu
Wang, Zihao
Zhang, Yong
Xing, Chunxiao
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2020), PT II, 2020, 12113 : 107 - 124

← 1 2 3 4 5 →