scholarly journals Ensemble-learning approach for the classification of Levels Of Geometry (LOG) of building elements

2022 ◽  
Vol 51 ◽  
pp. 101497
Author(s):  
Jimmy Abualdenien ◽  
André Borrmann
Keyword(s):  
Ensemble Learning ◽  
Learning Approach ◽  
Building Elements

Classification of AAMI heartbeat classes with an interactive ELM ensemble learning approach

2015 ◽  
Vol 19 ◽  
pp. 56-67 ◽  
Author(s):  
Mohamad M. Al Rahhal ◽  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Salim Malek ◽  
Haikel Al-Hichri ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Learning Approach

Handling incomplete data classification using imputed feature selected bagging (IFBag) method

2021 ◽  
Vol 25 (4) ◽  
pp. 825-846
Author(s):  
Ahmad Jaffar Khan ◽  
Basit Raza ◽  
Ahmad Raza Shahid ◽  
Yogan Jaya Kumar ◽  
Muhammad Faheem ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Ensemble Learning ◽  
Incomplete Data ◽  
Missing Values ◽  
Learning Approach ◽  
Imputation Methods ◽  
Real World Datasets ◽  
Almost All ◽  
Bagging Ensemble

Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.

scholarly journals FUSION OF BAGGING BASED ENSEMBLE FRAMEWORK FOR EPILEPTIC SEIZURE CLASSIFICATION

Transmisi ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 102-106
Author(s):  
Farrikh Alzami ◽  
Aries Jehan Tamamy ◽  
Ricardus Anggi Pramunendar ◽  
Zaenal Arifin
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Epileptic Seizure ◽  
Learning Approach ◽  
Fusion Rules ◽  
Weak Learner ◽  
Real World Datasets ◽  
Seizure Classification ◽  
Bagging Ensemble

The ensemble learning approach, especially in classification, has been widely carried out and is successful in many scopes, but unfortunately not many ensemble approaches are used for the detection and classification of epilepsy in biomedical terms. Compared to using a simple bagging ensemble framework, we propose a fusion bagging-based ensemble framework (FBEF) that uses 3 weak learners in each oracle, using fusion rules, a weak learner will give results as predictors of the oracle. All oracle predictors will be included in the trust factor to get a better prediction and classification. Compared to traditional Ensemble bagging and single learner type Ensemble bagging, our framework outperforms similar research in relation to the epileptic seizure classification as 98.11±0.68 and several real-world datasets

Sign in / Sign up

Export Citation Format

Share Document