An Ensemble Learning Approach for Automatic Emotion Classification of Sri Lankan Folk Music

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.

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

Advanced Engineering Informatics ◽

10.1016/j.aei.2021.101497 ◽

2022 ◽

Vol 51 ◽

pp. 101497

Author(s):

Jimmy Abualdenien ◽

André Borrmann

Keyword(s):

Ensemble Learning ◽

Learning Approach ◽

Building Elements

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An ensemble learning approach for the classification of remote sensing scenes based on covariance pooling of CNN features

2019 27th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco.2019.8902561 ◽

2019 ◽

Cited By ~ 2

Author(s):

Sara Akodad ◽

Solene Vilfroy ◽

Lionel Bombrun ◽

Charles C. Cavalcante ◽

Christian Germain ◽

...

Keyword(s):

Remote Sensing ◽

Ensemble Learning ◽

Learning Approach

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An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

IEEE Access ◽

10.1109/access.2021.3057196 ◽

2021 ◽

Vol 9 ◽

pp. 24485-24498

Author(s):

Davide Chicco ◽

Giuseppe Jurman

Keyword(s):

Ensemble Learning ◽

Learning Approach

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FUSION OF BAGGING BASED ENSEMBLE FRAMEWORK FOR EPILEPTIC SEIZURE CLASSIFICATION

Transmisi ◽

10.14710/transmisi.22.3.102-106 ◽

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

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Supervised Learning Approach for Classification of Sri Lankan Music based on Music Structure Similarity

10.5176/2251-1679_cgat16.25 ◽

2016 ◽

Author(s):

Lakshman Jayaratne ◽

◽

Rajitha Peiris

Keyword(s):

Supervised Learning ◽

Learning Approach ◽

Sri Lankan ◽

Music Structure ◽

Structure Similarity

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THE CLASSIC GENRE OF THE ORAL TRADITION OF AZERBAIJAN IS "MUGHAM"

Global problems of modernity ◽

10.26787/nydha-2713-2048-2020-1-10-11-12-6-10 ◽

2020 ◽

pp. 6-10

Author(s):

Mammadov R.

Keyword(s):

Folk Music ◽

Oral Tradition ◽

Single System ◽

Musical Culture ◽

Musical Tradition

This article is dedicated to the classical mugham genre. It analyzes the main genres of musical culture in Azerbaijan. The author studies the process of emergence of this genre, as well as its main types. Studies folk and national musical art, analyzes the classification of all genres of the oral musical tradition and combines them into a single system of genres of Azerbaijani folk music.

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