Hybrid ensembles of decision trees and Bayesian network for class imbalance problem

Ruangthong, Pumitara; Jaiyen, Saichon

doi:10.1109/kst.2016.7440523

Cited by 6 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oliveira et al (2015) proposed a technique for generating classifier ensembles called Iterative Classifier Selection Bagging (ICS-Bagging) to solve class imbalance problem. Ruangthong and Jaiyen (2016) proposed a new hybrid ensemble model based on AdaBoost.M2 and adopt SMOTE algorithm to solve the class imbalance problem in order to predict the probability of term deposit from bank customers. Huang and Zhang (2016) proposed an improved ensemble learning method called AdaBoost with SMOTE to enhance the forecasting performance.…”

Section: Ensemble (Or) Hybrid Methodsmentioning

confidence: 99%

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Rekha¹,

Tyagi²,

Reddy³

2019

Journal of Computer Science

View full text Add to dashboard Cite

In today's world, most of the data (real world) is present in imbalanced form by nature. This is because of not having efficient algorithms to put this data (i.e., generated data by billion of internetconnected devices (IoTs)) in respective format. Imbalanced data poses a great challenge to (both) data mining and machine learning algorithms. The imbalanced dataset consists of a majority class and a minority class, where the majority class takes the lead over the minority class. Generally, several standard learning algorithms assume the balanced class distribution or equal misclassification costs. If prediction is performed by these learning algorithms on imbalanced data, the accuracy will be high for majority classes, i.e., resulting in poor performance. To overcome this problem (or improving accuracy of deision/prediction-making process), data mining and machine learning researchers have addressed the problem of imbalanced data using datalevel, algorithmic level and ensemble or hybrid methods. This article presents a systematic literature review and analyze the results of more than 400 research papers published between 2002-2017 (till June 2017), resulting in a broader and elaborate investigation of the literature in this area of research. Note that extension of this article/work will contain till December 2018 research articles, which will be published in June 2019 (now these more papers/articles did not include due to no. of pages/space issues). The systematic analysis of the research literature has focus on the key role of Data Intrinsic Problems in classification, handling the imbalanced data and the techniques used to overcome the skewed distribution. Furthermore, this article reveals patterns, trends and gaps in the existing literature and discusses briefly the next generation research directions in this area.

show abstract

Section: Ensemble (Or) Hybrid Methodsmentioning

confidence: 99%

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Rekha¹,

Tyagi²,

Reddy³

2019

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…According to [26], the classifier's performance degrades when a dataset is class imbalanced. Therefore, they proposed a hybrid ensemble model to avoid unsatisfactory results from classification.…”

Section: Ensemble Learningmentioning

confidence: 99%

Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning

Aldelemy

Abd‐Alhameed

2023

View full text Add to dashboard Cite

The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. In this study, we develop a machine learning workflow for high classification accuracy and improved prediction confidence using a binary classification approach on a publicly available dataset from a Portuguese financial institution as a proof of concept. Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. We use stratified k-folding (k=5) cross-validation and assemble the top-performing classifiers into a decision-making committee, resulting in over 92% accuracy with two-thirds voting confidence. The workflow is simple, adaptable, and suitable for UK banks, demonstrating the potential for practical implementation and data privacy. Future work will extend our approach to UK banks, reformulate the problem as a multi-class classification, and introduce pre-training automated steps for data analysis and transformation.

show abstract

“…The visual recognition algorithm applies a learning process to construct a marker, covering the maximum of visual variations to facilitate the decision on when the images represent the same information or discarding those that do not represent any marker [27].…”

Section: ) Construction Of the Marker Based On Visual Informationmentioning

confidence: 99%

“…In the calculation, only values that remain within limits indicated by the decision layer are considered, dropping from the calculation the most discrepant values, and that can contaminate the final result, thus compensating the limitations of some of the models, as already described by Hanchuan [ 9]. (27) where M p is the weighted arithmetic mean, p 1 , p 2 , . .…”

Section: ) Dynamic Indoor Positioningmentioning

confidence: 99%

A Hybrid Indoor Positioning System Using a Linear Weighted Policy Learner and Iterative PDR

et al. 2020

View full text Add to dashboard Cite

show abstract

Hybrid ensembles of decision trees and Bayesian network for class imbalance problem

Cited by 6 publications

References 14 publications

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning

A Hybrid Indoor Positioning System Using a Linear Weighted Policy Learner and Iterative PDR

Contact Info

Product

Resources

About