Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

Agrawal, Shweta; Jain, Sanjiv; Khatri, Ajay; Agarwal, Mohit; Tripathi, Anshul; Hu, Yu‐Chen

doi:10.1155/2022/6445580

Cited by 2 publications

(3 citation statements)

References 42 publications

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“…Random forest has been shown to improve accuracy in various contexts, and the randomized selection of features in each tree enables its use in body performance analysis [5]. Bagging helps reduce the variance in the model by training separate models, while voting classifiers make decisions based on the majority of results from different classifiers [6]. Machine learning is a branch of artificial intelligence (AI) that adopts principles from computer science and statistics to create models that reflect patterns in data [7].…”

Section: Methodsmentioning

confidence: 99%

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Stacking classifier method for prediction of human body performance

Octavianto,

Wibowo

2024

IJEECS

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<p>A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.</p>

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Section: Methodsmentioning

confidence: 99%

“…There are two types of classifiers, including hard and soft voting. In hard voting the result is based on the majority, while for soft voting the result is based on the average of the votes in it [6].…”

Section: Voting Classifiermentioning

confidence: 99%

Stacking classifier method for prediction of human body performance

Octavianto,

Wibowo

2024

IJEECS

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“…Batta Mahesh provides a literature review on machine learning algorithms, noting the influence of problem type, variables, and suitable models on algorithm choice [6]. Shweta Agrawal et al propose a model for water quality assessment using machine learning, achieving high prediction accuracy through a voting classifier with hard voting [7]. Nur afyfah suwadi et al propose a feature selection method and compare machine learning models for water quality prediction, with XG Boost performing the best [8].…”

Section: IImentioning

confidence: 99%

Exploring Machine Learning Algorithms for Reliable Water Quality Prediction

Thakur,

Singh

2023

IJRASET

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Ensuring safe drinking water is a vital worldwide task. Accurate water quality prediction is crucial for protecting public health and the environment. Machine learning provides promising solutions for this objective. The study investigates the issue of precisely forecasting water quality with machine learning models. It examines different models and their efficacy in forecasting water quality features utilizing a given dataset. We conducted a comprehensive analysis of multiple machine learning models, including Bagging(REPTree), Multilayer Perceptron, M5P, Additive Regression, Stacking, Random Forest, and Decision Table. Firstly, ourselves imported the dataset into Weka, selected and configured the models, trained them on the dataset, and evaluated their performance using various metrics. Bagging (REPTree) outperformed compared to other models, showing its effectiveness in predicting water quality. Model selection depends on goals and constraints. Future research opportunities include feature engineering, ensemble methods, and data quality issues. The study concludes that Bagging (REPTree) classifier is a strong candidate for properly predicting water quality attributes. Future research should focus on improving feature engineering, exploring ensemble methods, expanding the dataset, and enhancing model explain ability. Deploying selected models for continuous monitoring and early detection can contribute to safer water supplies and sustainable water management practices. Compliance with water quality regulations can be better ensured through the application of these models. Overall, this study offers valuable insights regarding the application of machine learning for water quality prediction and highlights future directions for research and application in this important area.

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Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

Cited by 2 publications

References 42 publications

Stacking classifier method for prediction of human body performance

Stacking classifier method for prediction of human body performance

Exploring Machine Learning Algorithms for Reliable Water Quality Prediction

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