A supervised machine learning approach using naive Gaussian Bayes classification for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy (PALS)

Petschke, Danny; Staab, T.E.M.

doi:10.1016/j.nima.2019.162742

Cited by 18 publications

(7 citation statements)

References 26 publications

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“… Algorithm 1 GVFS method Input: dataset

with label

Output: feature sorted dataset

with label

= normalize (

); 2: Dividing the samples set into the training dataset and test dataset; 3: Calculate the

for each feature on each category of the training dataset [ 33 ]. 4: for i = 1:1:sample_number 5: Calculate the Gaussian probability for each feature on each category [ 33 ]. 6: The category with the largest GPDF is taken as the category for each feature.

…”

Section: Proposed Feature Selection Methodsmentioning

confidence: 99%

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A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

Wang

2022

Sensors

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For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Many redundant features will lead to a decrease in diagnosis efficiency and increase the computation cost, so it is necessary to eliminate redundant features and features that have negative effects. This paper presents a novel supervised filter feature selection method for reducing data dimension by employing the Gaussian probability density function (GPDF) and named Gaussian vote feature selection (GVFS). To evaluate the effectiveness of the proposed GVFS, we compared it with the other five filter feature selection methods by utilizing the PMDCM’s data. Additionally, Gaussian naive Bayes (GNB), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) are utilized for the construction of fault diagnosis models. Experimental results show that the proposed GVFS has a better diagnostic effect than the other five feature selection methods, and the average accuracy of fault diagnosis improves from 97.89% to 99.44%. This paper lays the foundation of fault diagnosis for PMDCMs and provides a novel filter feature selection method.

show abstract

“… Algorithm 1 GVFS method Input: dataset

with label

Output: feature sorted dataset

with label

= normalize (

); 2: Dividing the samples set into the training dataset and test dataset; 3: Calculate the

…”

Section: Proposed Feature Selection Methodsmentioning

confidence: 99%

“…This study aims at constructing an effective and feasible fault diagnosis model for permanent magnet DC motors (PMDCMs) based on Gaussian naive Bayes (GNB) [ 33 ],

-nearest neighbor algorithm (

-NN) [ 34 ], and the support vector machine (SVM) [ 35 ] by utilizing the filter feature selection methods.…”

Section: Experiments Settingmentioning

confidence: 99%

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

Wang

2022

Sensors

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“…It is based on the Bayes' theorem and assumes that f features are mutually independent. In practice, the independency assumption may be violated, but it significantly simplifies the classification task, without a dramatic reduction of prediction accuracy and robustness [16,32].…”

Section: Basics Of Naïve Bayesmentioning

confidence: 99%

Optimal Classifier to Detect Unit of Measure Inconsistency in Gas Turbine Sensors

et al. 2022

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Label noise is a harmful issue that arises when data are erroneously labeled. Several label noise issues can occur but, among them, unit of measure inconsistencies (UMIs) are inexplicably neglected in the literature. Despite its relevance, a general and automated approach for UMI detection suitable to gas turbines (GTs) has not been developed yet; as a result, GT diagnosis, prognosis, and control may be challenged since collected data may not reflect the actual operation. To fill this gap, this paper investigates the capability of three supervised machine learning classifiers, i.e., Support Vector Machine, Naïve Bayes, and K-Nearest Neighbors, that are tested by means of challenging analyses to infer general guidelines for UMI detection. Classification accuracy and posterior probability of each classifier is evaluated by means of an experimental dataset derived from a large fleet of Siemens gas turbines in operation. Results reveal that Naïve Bayes is the optimal classifier for UMI detection, since 88.5% of data are correctly labeled with 84% of posterior probability when experimental UMIs affect the dataset. In addition, Naïve Bayes proved to be the most robust classifier also if the rate of UMIs increases.

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“…Chen et al [27] similarly applied the method that proposed two approaches for exploring the preferred prior GNB settings considering the individual impacts of the predictors. Shown in equation 2 is the concept of Naïve Bayes [28].…”

Section:   mentioning

confidence: 99%

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

Lauguico

Concepcion

Alejandrino

et al. 2020

Int. J. Adv. Intell. Informatics

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Classification of lettuce life or growth stages is an effective tool for measuring the performance of an aquaponics system. It determines the balance in water nutrients, adequate temperature and lighting, other environmental factors, and the system’s productivity to sustain cultivars. This paper proposes a classification of lettuce life stages planted in an aquaponics system. The classification was done using the texture features of the leaves derived from machine vision algorithms. The attributes underwent three different feature selection processes, namely: Univariate Selection (US), Recursive Feature Elimination (RFE), and Feature Importance (FI) to determine the four most significant features from the original eight attributes. The features selected were used for training four estimators from Decision Trees Classifier (DTC), Gaussian Naïve Bayes (GNB), Stochastic Gradient Descent (SGD), and Linear Discriminant Analysis (LDA). The models trained using DTC and SGD were then optimized as they have hyperparameters for tuning. A comparative analysis among Machine Learning (ML) algorithms was conducted to identify the best-performing model with the given application. The best features were derived from US and FI as they have the same top four features using the DTC estimator optimized with the hyperparameters tuned to max depth having 5, criterion equated to ‘Gini', and splitter was set to 'Best'. The accuracy obtained from cross-validation evaluation resulted in 87.92%. Considering consistency with hold-out validation, LDA outperforms optimized DTC even with lower accuracy of 86.67%. This accuracy of LDA outperformed DTC due to its sufficient fit for generalizing the testing data on classifying lettuce growth stage.

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A supervised machine learning approach using naive Gaussian Bayes classification for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy (PALS)

Cited by 18 publications

References 26 publications

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

Optimal Classifier to Detect Unit of Measure Inconsistency in Gas Turbine Sensors

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

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