Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation

Habib, Maan; Okayli, Maan

doi:10.1007/s13369-024-08776-2

Cited by 11 publications

(1 citation statement)

References 34 publications

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“…The algorithm continues to the next step as long as there has been an improvement in performance in the last p iterations. The Backward Elimination technique begins with an attribute set that includes all features [64,65]. It evaluates all attribute sets and chooses the top k sets based on their performance.…”

Section: Feature Selectionmentioning

confidence: 99%

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Mostafa,

Mahmoud,

Abd El-Hafeez

et al. 2024

J Big Data

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Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates accurate prediction models for early diagnosis and effective treatment. Machine learning algorithms have demonstrated promising results in various medical domains, including cancer prediction. In this study, we propose a comprehensive approach for HCC prediction by comparing the performance of different machine learning algorithms before and after applying feature reduction methods. We employ popular feature reduction techniques, such as weighting features, hidden features correlation, feature selection, and optimized selection, to extract a reduced feature subset that captures the most relevant information related to HCC. Subsequently, we apply multiple algorithms, including Naive Bayes, support vector machines (SVM), Neural Networks, Decision Tree, and K nearest neighbors (KNN), to both the original high-dimensional dataset and the reduced feature set. By comparing the predictive accuracy, precision, F Score, recall, and execution time of each algorithm, we assess the effectiveness of feature reduction in enhancing the performance of HCC prediction models. Our experimental results, obtained using a comprehensive dataset comprising clinical features of HCC patients, demonstrate that feature reduction significantly improves the performance of all examined algorithms. Notably, the reduced feature set consistently outperforms the original high-dimensional dataset in terms of prediction accuracy and execution time. After applying feature reduction techniques, the employed algorithms, namely decision trees, Naive Bayes, KNN, neural networks, and SVM achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00%, respectively.

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Section: Feature Selectionmentioning

confidence: 99%

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Mostafa,

Mahmoud,

Abd El-Hafeez

et al. 2024

J Big Data

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The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction

Demir,

Sahin

2024

Stoch Environ Res Risk Assess

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In the field of data engineering in machine learning (ML), a crucial component is the process of scaling, normalization, and standardization. This process involves transforming data to make it more compatible with modeling techniques. In particular, this transformation is essential to ensure the suitability of the data for subsequent analysis. Despite the application of many conventional and relatively new approaches to ML, there remains a conspicuous lack of research, particularly in the geotechnical discipline. In this study, ML-based prediction models (i.e., RF, SVR, Cubist, and SGB) were developed to estimate the undrained shear strength (UDSS) of cohesive soil from the perspective of a wide range of data-scaling and transformation methods. Therefore, this work presents a novel ML framework based on data engineering approaches and the Cubist regression method to predict the UDSS of cohesive soil. A dataset including six different features and one target variable were used for building prediction models. The performance of ML models was examined considering the impact of the data pre-processing issue. For that purpose, data scaling and transformation methods, namely Range, Z-Score, Log Transformation, Box-Cox, and Yeo-Johnson, were used to generate the models. The results were then systematically compared using different sampling ratios to understand how model performance varies as various data scaling/transformation methods and ML algorithms were combined. It was observed that data transformation or data sampling methods had considerable or limited effects on the UDSS model performance depending on the algorithm type and the sampling ratio. Compared to RF, SVR, and SGB models, Cubist models provided higher performance metrics after applying the data pre-processing steps. The Box-Cox transformed Cubist model yielded the best prediction performance among the other models with an R2 of 0.87 for the 90% training set. Also, the UDSS prediction model generally yielded the best performance metrics when it was used with the transformed-based models (i.e., Box-Cox, Log, and Yeo-Johnson) than that of scaled-based (i.e., Range and Z-Score) models. The results show that the Cubist model has a higher potential for UDSS prediction, and data pre-processing methods have impacts on the predictive capacity of the evaluated regression models.

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A comparative analysis of decision tree on torsional capacity of reinforced concrete beams

Li,

Chen

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation

Cited by 11 publications

References 34 publications

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction

A comparative analysis of decision tree on torsional capacity of reinforced concrete beams

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