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

页码：375 / 382

页数：8

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] A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers
Mehtab Ur Rahman
Cem Direkoglu
BMC Medical Informatics and Decision Making, 25 (1)
[10] Efficient classifiers for multi-class classification problems
Lin, Hung-Yi
DECISION SUPPORT SYSTEMS, 2012, 53 (03) : 473 - 481

← 1 2 3 4 5 →