New feature selection and voting scheme to improve classification accuracy

Tsai, Cheng Jung

doi:10.1007/s00500-019-03757-2

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…In this study, four methods proposed by scholars were utilized to enhance the diversity of classification models (or classifiers) within the ensemble learning model, including the use of different training datasets and training of different classification models with different parameter settings, algorithms, and characteristic factors [31,38]. In previous studies, five algorithms were used to construct the ensemble learning model EL V.1.…”

Section: Improvement Of Ensemble Learning Modelmentioning

confidence: 99%

EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method

Liu

Weng

et al. 2023

Foods

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Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan’s border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the “detection rate of unqualified cases” and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, Fβ was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of “pre-launch (2019) random sampling inspection” and “post-launch (2020–2022) model prediction sampling inspection”. For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.

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Section: Improvement Of Ensemble Learning Modelmentioning

confidence: 99%

EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method

Liu

Weng

et al. 2023

Foods

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“…Four key methods have been proposed to enhance the diversity of classification models used in ensemble learning: (1) using different training datasets, (2) adopting different parameter settings for various classification models, (3) using different algorithms to train different classification models, and (4) using different features for classification model training [9,13,14].…”

Section: Principles Of Ensemble Learningmentioning

confidence: 99%

Ensemble Learning Models for Food Safety Risk Prediction

Weng

2021

Sustainability

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Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.

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“…Feature selection is an important task that affects the performance of classification models [18,19]. Genetic algorithms (GA) is a nature inspired and heuristic algorithm that is utilized for search optimization problems and has been widely adopted to find the optimum feature subsets toward improving the performance of models [24,25].…”

Section: Extreme Gradient Boosting (Xgboost) and Genetic Algorithms (Ga)mentioning

confidence: 99%

“…Previous researchers have reported the advantage of XGBoost as classification model for predicting hepatitis B virus infection [13], gestational diabetes mellitus of early pregnant women [14], future blood glucose level of type 1 diabetes (T1D) patients [15], coronary artery calcium score (CACS) [16], and heart disease prediction [17]. However, in the machine learning research, the performance of the classification model may be influenced by unrelated attributes or features [18,19]. Feature selection is used to reduce the dimensionality of data [20,21] and in medical diagnosis, is used to identify most significant features related to disease [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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New feature selection and voting scheme to improve classification accuracy

Cited by 9 publications

References 28 publications

EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method

EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method

Ensemble Learning Models for Food Safety Risk Prediction

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

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