A New Bayesian Network Based on Gaussian Naive Bayes with Fuzzy Parameters for Training Assessment in Virtual Simulators

Moraes, Ronei Marcos de; Ferreira, Jodavid A.; Machado, Liliane S.

doi:10.1007/s40815-020-00936-4

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In order to compare the performance stability between machine learning models, this study uses eight widely used machine learning models, including Gaussian Naive Bayes (GNB) [21], Bernoulli naive Bayes (BNB) [22], K-nearest neighbor (KNN) [23], logistic regression (LR) [24], random forest (RF) [25], decision tree (DT) [26], gradient boosting decision tree [27] and support vector classifier (SVC) [28], used as classification algorithms to train and predict imbalanced data. These machine learning models were implemented based on the Python library Scikit-Learn [29] with default settings employed.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models

Zheng

Wang

et al. 2022

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Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine learning models. It systematically analyzes 1. the relationship between varying performance in machine learning models and imbalance rate (IR); 2. the performance stability of machine learning models on imbalanced binary data. In the proposed method, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced dataset with gradually varying IR. Then, in order to obtain more objective classification results, the evaluation metric AFG, arithmetic mean of area under the receiver operating characteristic curve (AUC), F-measure and G-mean are used to evaluate the classification performance of machine learning models. Finally, based on AFG and coefficient of variation (CV), the performance stability evaluation method of machine learning models is proposed. Experiments of eight widely used machine learning models on 48 different imbalanced datasets demonstrate that the classification performance of machine learning models decreases with the increase of IR on the same imbalanced data. Meanwhile, the classification performances of LR, DT and SVC are unstable, while GNB, BNB, KNN, RF and GBDT are relatively stable and not susceptible to imbalanced data. In particular, the BNB has the most stable classification performance. The Friedman and Nemenyi post hoc statistical tests also confirmed this result. The SMOTE method is used in oversampling-based imbalanced data augmentation, and determining whether other oversampling methods can obtain consistent results needs further research. In the future, an imbalanced data augmentation algorithm based on undersampling and hybrid sampling should be used to analyze the performance impact of imbalanced binary data on machine learning models.

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Section: Machine Learning Modelsmentioning

confidence: 99%

A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models

Zheng

Wang

et al. 2022

Axioms

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Which standard classification algorithm has more stable performance for imbalanced network traffic data?

Zheng,

Ma,

Wang

et al. 2023

Soft Comput

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Alpha Skew Gaussian Naïve Bayes Classifier

Ara

Louzada

2021

Int. J. Info. Tech. Dec. Mak.

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The main goal of this paper is to introduce a new procedure for a naïve Bayes classifier, namely alpha skew Gaussian naïve Bayes (ASGNB), which is based on a flexible generalization of the Gaussian distribution applied to continuous variables. As a direct advantage, this method can accommodate the possibility to handle with asymmetry in the uni or bimodal behavior. We provide the estimation procedure of this method, and we check the predictive performance when compared to other traditional classification methods using simulation studies and many real datasets with different application fields. The ASGNB is a powerful alternative to classification tasks when lie the presence of asymmetry of bimodality in the data and outperforms well when compared to other traditional classification methods in most of the cases analyzed.

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A New Bayesian Network Based on Gaussian Naive Bayes with Fuzzy Parameters for Training Assessment in Virtual Simulators

Cited by 3 publications

References 20 publications

A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models

A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models

Which standard classification algorithm has more stable performance for imbalanced network traffic data?

Alpha Skew Gaussian Naïve Bayes Classifier

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