Locality in Multi-label Classification Problems

被引：0
|
作者：
Norov-Erdene, Batzaya [1 ]
Kudo, Mineichi [1 ]
Sun, Lu [1 ]
Kimura, Keigo [1 ]
机构：
[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Div Comp Sci & Informat Technol, Sapporo, Hokkaido 060081, Japan
来源：
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2016年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Lately, multi-label classification (MLC) problems have drawn a lot of attention in a wide range of fields including medical, web, and entertainment. The scale and the diversity of MLC problems is much larger than single-label classification problems. Especially we have to face all possible combinations of labels. To solve MLC problems more efficiently, we focus on three kinds of locality hidden in a given MLC problem. In this paper, first we show how large degree of locality exists in nine datasets, then examine how closely they are related to labels, and last propose a method of reducing the problem size using one kind of locality.
引用
收藏
页码：2319 / 2324
页数：6
相关论文
共 50 条
  • [1] Feature Selection for Multi-label Classification Problems
    Doquire, Gauthier
    Verleysen, Michel
    [J]. ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2011, PT I, 2011, 6691 : 9 - 16
  • [2] Dynamic feature weighting for multi-label classification problems
    Maryam Dialameh
    Ali Hamzeh
    [J]. Progress in Artificial Intelligence, 2021, 10 : 283 - 295
  • [3] Hierarchical Multi-label Classification Problems: An LCS Approach
    Romao, Luiz Melo
    Nievola, Julio Cesar
    [J]. Distributed Computing and Artificial Intelligence, 12th International Conference, 2015, 373 : 97 - 104
  • [4] A Classification Approach with a Reject Option for Multi-label Problems
    Pillai, Ignazio
    Fumera, Giorgio
    Roli, Fabio
    [J]. IMAGE ANALYSIS AND PROCESSING - ICIAP 2011, PT I, 2011, 6978 : 98 - 107
  • [5] Dynamic feature weighting for multi-label classification problems
    Dialameh, Maryam
    Hamzeh, Ali
    [J]. PROGRESS IN ARTIFICIAL INTELLIGENCE, 2021, 10 (03) : 283 - 295
  • [6] Label Expansion for Multi-Label Classification
    Rivolli, Adriano
    Soares, Carlos
    de Carvalho, Andre C. P. L. F.
    [J]. 2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 2018, : 414 - 419
  • [7] Research on multi-label classification problems based on neural networks and label correlation
    Jia, Ling
    Fan, Jin
    Sun, Dong
    Gao, Qingwei
    Lu, Yixiang
    [J]. 2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 7298 - 7302
  • [8] MLCE: A Multi-Label Crotch Ensemble Method for Multi-Label Classification
    Yao, Yuan
    Li, Yan
    Ye, Yunming
    Li, Xutao
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2021, 35 (04)
  • [9] Multi-label classification of feedbacks
    Alonso, Dorian Ruiz
    Cortés, Claudia Zepeda
    Zacatelco, Hilda Castillo
    Carranza, José Luis Carballido
    Cué, José Luis García
    [J]. Journal of Intelligent and Fuzzy Systems, 2022, 42 (05): : 4337 - 4343
  • [10] The advances in multi-label classification
    Chen, Shijun
    Gao, Lin
    [J]. 2014 INTERNATIONAL CONFERENCE ON MANAGEMENT OF E-COMMERCE AND E-GOVERNMENT (ICMECG), 2014, : 240 - 245
12345