A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification

被引：0
|
作者：
Takenouchi, Takashi [1 ]
Ishii, Shin [2 ]
机构：
[1] Future Univ Hakodate, 116-2 Kamedanakano, Hakodate, Hokkaido 0418655, Japan
[2] Kyoto Univ, Grad Sch Informat, Uji, Kyoto 6100011, Japan
来源：
NEURAL INFORMATION PROCESSING, ICONIP 2012, PT II | 2012年 / 7664卷
关键词：
Ensemble learning; Mixture of divergences;
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：
We present a novel methods for multi-class classification by ensemble of binary classifiers for multi-class classification. The proposed method is characterized by a minimization problem of weighted divergences, and includes a lot of conventional methods as special cases. We discuss relationship between the proposed method and conventional methods and statistical properties of the proposed method. A small experiment shows that the proposed method can effectively incorporate information of multiple binary classifiers into multi-class classifier.
引用
收藏
页码：375 / 382
页数：8
相关论文
共 50 条
  • [1] Binary classifiers ensemble based on Bregman divergence for multi-class classification
    Takenouchi, Takashi
    Ishii, Shin
    NEUROCOMPUTING, 2018, 273 : 424 - 434
  • [2] Statistical approaches to combining binary classifiers for multi-class classification
    Shiraishi, Yuichi
    Fukumizu, Kenji
    NEUROCOMPUTING, 2011, 74 (05) : 680 - 688
  • [3] Multi-class classification via heterogeneous ensemble of one-class classifiers
    Kang, Seokho
    Cho, Sungzoon
    Rang, Pilsung
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 43 : 35 - 43
  • [4] Greedy hierarchical binary classifiers for multi-class classification of biological data
    Salma Begum
    Ramazan S. Aygun
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2014, 3 (1)
  • [5] Greedy hierarchical binary classifiers for multi-class classification of biological data
    Begum, Salma
    Aygun, Ramazan S.
    NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, 2014, 3 (01):
  • [6] Topological embedding and directional feature importance in ensemble classifiers for multi-class classification
    Liedl, Eloisa Rocha
    Yassin, Shabeer Mohamed
    Kasapi, Melpomeni
    Posma, Joram M.
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2024, 23 : 4108 - 4123
  • [7] Multi-class pattern classification based on a probabilistic model of combining binary classifiers
    Yukinawa, N
    Oba, S
    Kato, K
    Ishii, S
    ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS, 2005, 3697 : 337 - 342
  • [8] Blind Multi-class Ensemble Learning with Dependent Classifiers
    Traganitis, Panagiotis A.
    Giannakis, Georgios B.
    2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2018, : 2025 - 2029
  • [9] Efficient classifiers for multi-class classification problems
    Lin, Hung-Yi
    DECISION SUPPORT SYSTEMS, 2012, 53 (03) : 473 - 481
  • [10] QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
    Silver, Daniel
    Patel, Tirthak
    Tiwari, Devesh
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 8324 - 8332
12345