Selection of discriminant mid-infrared wavenumbers by combining a naïve Bayesian classifier and a genetic algorithm: Application to the evaluation of lignocellulosic biomass biodegradation
“…The GA steps reproduce the various evolutionary operations such as crossover and mutation allowing to select for each generation the best chromosomes and to identify at the end an optimal chromosome with respect to an optimization criteria defined by a fitness function 29 . Figure 1 shows the steps of the informative feature selection procedure using a GA 30 . Figure 1 Synoptic representation of the proposed GA methodology.…”
Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.
“…The GA steps reproduce the various evolutionary operations such as crossover and mutation allowing to select for each generation the best chromosomes and to identify at the end an optimal chromosome with respect to an optimization criteria defined by a fitness function 29 . Figure 1 shows the steps of the informative feature selection procedure using a GA 30 . Figure 1 Synoptic representation of the proposed GA methodology.…”
Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.
“…We evaluated the most common ML models, namely, discriminant analysis classification (DAC) [9], decision tree classification (DTC) [10], K-nearest neighbor classification (KNNC) [11,12], naïve Bayes classification (NBC) [13,14], random forest classification (RFC) [15,16], and support vector machine classification (SVMC) [17,18].…”
Background: Machine learning (ML) is a promising methodology for classification and prediction applications in healthcare. However, this method has not been practically established for clinical data. Hyperuricemia is a biomarker of various chronic diseases. We aimed to predict uric acid status from basic healthcare checkup test results using several ML algorithms and to evaluate the performance. Methods: We designed a prediction model for hyperuricemia using a comprehensive health checkup database designed by the classification of ML algorithms, such as discrimination analysis, K-nearest neighbor, naïve Bayes (NBC), support vector machine, decision tree, and random forest classification (RFC). The performance of each algorithm was evaluated and compared with the performance of a conventional logistic regression (CLR) algorithm by receiver operating characteristic curve analysis. Results: Of the 38,001 participants, 7705 were hyperuricemic. For the maximum sensitivity criterion, NBC showed the highest sensitivity (0.73), and RFC showed the second highest (0.66); for the maximum balanced classification rate (BCR) criterion, RFC showed the highest BCR (0.68), and NBC showed the second highest (0.66) among the various ML algorithms for predicting uric acid status. In a comparison to the performance of NBC (area under the curve (AUC) = 0.669, 95% confidence intervals (CI) = 0.669–0.675) and RFC (AUC = 0.775, 95% CI 0.770–0.780) with a CLR algorithm (AUC = 0.568, 95% CI = 0.563–0.571), NBC and RFC showed significantly better performance (p < 0.001). Conclusions: The ML model was superior to the CLR model for the prediction of hyperuricemia. Future studies are needed to determine the best-performing ML algorithms based on data set characteristics. We believe that this study will be informative for studies using ML tools in clinical research.
“…Various supervised machine learning algorithms exist in the literature. The best-known are Linear Discriminant Analysis (LDA) [30], Naïve Bayes Classification (NBC) [31], Decision Tree Classification (DTC) [32,33], and Support Vector Machine Classification (SVM) [34],…”
The COVID-19 virus spread from China throughout the world, causing out of control challenges to the public health community. During the epidemic, it is difficult to act against infectious disease due to its unknown trends, so the spread prediction becomes difficult in light of the scarce data. With the absence of treatment for COVID-19 infection, countries have taken some mitigation steps and policies, such as general lockdown and social distance measures, but there has been variation in the extent of viral spread due to several additional factors. Prediction methods based on Artificial intelligence (AI) and Machine Learning (ML) can help in suggesting new policies or even assessing the effectiveness of the existing ones. Such methods have attracted wide attention from researchers implementing statistical modeling and machine learning methods. The objective of this study is to examine different supervised classification approaches to detect the degrees of possibility of Coronavirus disease infection in different countries. Naïve Bayesian Classifier (NBC), Decision Tree Classifier (DTC), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Artificial Neural Network classification (ANN) are machine learning algorithms used for the prediction of coronavirus disease cases according to the time of their evolution while considering data collected from official reports and scientific journals. Since we collected mixed data, we suggested to also apply the supervised classifiers available for mixed data, such as Extended Gamma (EG) and Naive Associative Classifier (NAC). The results showed that ANN and DTC supervised algorithms allow better discrimination between the degrees of possibility of Coronavirus disease infection among advanced methods like NBC, SVM, NAC, EG and LDA.
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