Multi-view Ensemble Learning for Poem Data Classification Using SentiWordNet

2014 ◽  
pp. 57-66 ◽  
Author(s):  
Vipin Kumar ◽  
Sonajharia Minz
Keyword(s):  
Ensemble Learning ◽  
Data Classification

Performance assessment of ensemble learning systems in financial data classification

2020 ◽  
Vol 27 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Salim Lahmiri ◽  
Stelios Bekiros ◽  
Anastasia Giakoumelou ◽  
Frank Bezzina
Keyword(s):  
Performance Assessment ◽  
Ensemble Learning ◽  
Data Classification ◽  
Learning Systems ◽  
Financial Data

scholarly journals Rough set based ensemble learning algorithm for agricultural data classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1917-1930 ◽  
Author(s):  
Lei Shi ◽  
Qiguo Duan ◽  
Juanjuan Zhang ◽  
Lei Xi ◽  
Hongbo Qiao ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Rough Set ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
Uncertain Data ◽  
Data Classification ◽  
Research Area ◽  
Experimental Comparison ◽  
Classification Problems ◽  
Ensemble Learning Algorithm

Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

Ensemble Learning via Extreme Learning Machines for Imbalanced Data

2020 ◽  
pp. 59-88
Author(s):  
Adnan Omer Abuassba ◽  
Dezheng O. Zhang ◽  
Xiong Luo
Keyword(s):  
Ensemble Learning ◽  
Imbalanced Data ◽  
Noisy Data ◽  
Data Classification ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Learning Machine ◽  
Heterogeneous Ensemble

Ensembles are known to reduce the risk of selecting the wrong model by aggregating all candidate models. Ensembles are known to be more accurate than single models. Accuracy has been identified as an important factor in explaining the success of ensembles. Several techniques have been proposed to improve ensemble accuracy. But, until now, no perfect one has been proposed. The focus of this research is on how to create accurate ensemble learning machine (ELM) in the context of classification to deal with supervised data, noisy data, imbalanced data, and semi-supervised data. To deal with mentioned issues, the authors propose a heterogeneous ELM ensemble. The proposed heterogeneous ensemble of ELMs (AELME) for classification has different ELM algorithms, including regularized ELM (RELM) and kernel ELM (KELM). The authors propose new diverse AdaBoost ensemble-based ELM (AELME) for binary and multiclass data classification to deal with the imbalanced data issue.

A synthetic neighborhood generation based ensemble learning for the imbalanced data classification

2017 ◽  
Vol 48 (8) ◽  
pp. 2441-2457 ◽  
Author(s):  
Zhi Chen ◽  
Tao Lin ◽  
Xin Xia ◽  
Hongyan Xu ◽  
Sha Ding
Keyword(s):  
Ensemble Learning ◽  
Imbalanced Data ◽  
Data Classification ◽  
Imbalanced Data Classification

Hybrid Incremental Ensemble Learning for Noisy Real-World Data Classification

2019 ◽  
Vol 49 (2) ◽  
pp. 403-416 ◽  
Author(s):  
Zhiwen Yu ◽  
Daxing Wang ◽  
Zhuoxiong Zhao ◽  
C. L. Philip Chen ◽  
Jane You ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Data Classification ◽  
Real World Data ◽  
World Data
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