Bagging and boosting variants for handling classifications problems: a survey

AbstractBagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. Since bagging and boosting are an effective and open framework, several researchers have proposed their variants, some of which have turned out to have lower classification error than the original versions. This paper tried to summarize these variants and categorize them into groups. We hope that the references cited cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods, guiding the researcher in interesting research directions.

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A Study of Tangerine Pest Recognition Using Advanced Deep Learning Methods

10.20944/preprints201811.0161.v1 ◽

2018 ◽

Cited By ~ 2

Author(s):

Marely Lee ◽

Shuli Xing

Keyword(s):

Deep Learning ◽

Crop Yield ◽

Nearest Neighbors ◽

Experimental Results ◽

Classification Error ◽

Learning Methods ◽

K Nearest Neighbors ◽

Minimum Classification Error ◽

Citrus Pests

To improve the tangerine crop yield, the work of recognizing and then disposing of specific pests is becoming increasingly important. The task of recognition is based on the features extracted from the images that have been collected from websites and outdoors. Traditional recognition and deep learning methods, such as KNN (k-nearest neighbors) and AlexNet, are not preferred by knowledgeable researchers, who have proven them inaccurate. In this paper, we exploit four kinds of structures of advanced deep learning to classify 10 citrus pests. The experimental results show that Inception-ResNet-V3 obtains the minimum classification error.

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Review for "Prediction of the reflection intensity of natural hydroxyapatite using generalized linear model and ensemble learning methods"

10.1002/eng2.12292/v1/review4 ◽

2020 ◽

Keyword(s):

Linear Model ◽

Ensemble Learning ◽

Generalized Linear Model ◽

Learning Methods ◽

Natural Hydroxyapatite ◽

Reflection Intensity

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Author response for "Prediction of the reflection intensity of natural hydroxyapatite using generalized linear model and ensemble learning methods"

10.1002/eng2.12292/v3/response1 ◽

2020 ◽

Author(s):

Emmanuel Okafor ◽

David O. Obada ◽

Yusuf Ibrahim ◽

David Dodoo‐Arhin

Keyword(s):

Linear Model ◽

Ensemble Learning ◽

Generalized Linear Model ◽

Author Response ◽

Learning Methods ◽

Natural Hydroxyapatite ◽

Reflection Intensity

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Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints

Toxicology Letters ◽

10.1016/j.toxlet.2020.07.003 ◽

2020 ◽

Vol 332 ◽

pp. 88-96 ◽

Cited By ~ 1

Author(s):

Miao Liu ◽

Li Zhang ◽

Shimeng Li ◽

Tianzhou Yang ◽

Lili Liu ◽

...

Keyword(s):

Potassium Channel ◽

Ensemble Learning ◽

Learning Methods ◽

Molecular Fingerprints ◽

Channel Blockage

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Ensemble learning methods for decision making: Status and future prospects

2015 International Conference on Machine Learning and Cybernetics (ICMLC) ◽

10.1109/icmlc.2015.7340924 ◽

2015 ◽

Cited By ~ 6

Author(s):

Shahid Ali ◽

Sreenivas Sremath Tirumala ◽

Abdolhossein Sarrafzadeh

Keyword(s):

Decision Making ◽

Ensemble Learning ◽

Future Prospects ◽

Learning Methods

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The psychological imagination

Psicologia USP ◽

10.1590/0103-6564d20160008 ◽

2016 ◽

Vol 27 (2) ◽

pp. 229-233 ◽

Cited By ~ 2

Author(s):

Luca Tateo

Keyword(s):

Human Activity ◽

Special Issue ◽

Research Directions ◽

Dialogical Self ◽

Dialogical Self Theory ◽

Higher Psychological Functions ◽

New Research ◽

The Relationship ◽

Theoretical Issues ◽

Symbolic Products

Abstract: The commentary presents an epistemological reflection about Dialogical Self theory. First, the theoretical issues of DS about the relationship between individuality, alterity and society are discussed, elaborating on the articles of this special issue. Then, it is presented the argument of psychologist's ontological fallacy, that is the attitude to moving from the study of processes to the study of psychological entities. Finally a development toward new research directions is proposed, focusing on the study of higher psychological functions and processes, taking into account complex symbolic products of human activity and developing psychological imagination.

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Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

Asian Business Research ◽

10.20849/abr.v2i2.147 ◽

2017 ◽

Vol 2 (2) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Jing Jiang ◽

Hua-Ming Song

Keyword(s):

Process Control ◽

Decision Tree ◽

Statistical Process Control ◽

Ensemble Learning ◽

Experimental Results ◽

Multivariate Statistical Process Control ◽

Multivariate Statistical ◽

Statistical Process ◽

Multiple Classifiers ◽

Control Signals

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

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