Noise simulation in classification with the noisemodel R package: Applications analyzing the impact of errors with chemical data

Sáez, Juan Manuel

doi:10.1002/cem.3472

Cited by 8 publications

(3 citation statements)

References 71 publications

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“…Another recent comprehensive review of diferent methods of adding noise to class variable, attribute variables, or both in combination is given by Sáez [28]. Sáez has also presented an R package which is called noisemodel [29]. Tis R package contains diferent ways for adding noise to class variable, attribute variables, and both in combination.…”

Section: Adding Noise Methodsmentioning

confidence: 99%

Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Alharbi

2024

Discrete Dynamics in Nature and Society

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Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.

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Section: Adding Noise Methodsmentioning

confidence: 99%

Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Alharbi

2024

Discrete Dynamics in Nature and Society

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“…Conducting further studies could also provide a more nuanced understanding of PU development over time, considering factors such as lesion severity and changes in patient characteristics. Moreover, as inaccuracies can impact the performance, complexity, and construction time of classifiers [53], investigating different data preprocessing techniques becomes relevant. Addressing issues such as class overlapping or noise through approaches like noise filters [54] could contribute to further improvements in the models created.…”

Section: Future Research Directionsmentioning

confidence: 99%

Differentiating Pressure Ulcer Risk Levels through Interpretable Classification Models Based on Readily Measurable Indicators

Vera-Salmerón,

Domínguez-Nogueira,

Sáez

et al. 2024

Healthcare

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Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient’s requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level.

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“…Garcia et al [10] demonstrated the its efficacy in replicating noise patterns observed in real-world datasets, confirming its statistical properties and impact on model performance. Multiple empirical studies have shown that the Neighborwise method enhances the robustness of ML models by providing a realistic basis for evaluating noise filters [38]. In the current work, we utilized the Neighborwise method to create controlled yet realistic noise conditions to improve the accuracy and robustness of our noise filter and hyperparameter recommendations for CNL tasks.…”

Section: Noise Imputationmentioning

confidence: 99%

MtL-NFW: A Meta-Learning Framework for Automated Noise Filter Selection and Hyperparameter Optimization in Auto-ML

Khan,

Zhang,

Kumar

et al. 2024

Preprint

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The extensive implementation of machine learning (ML) has transformed data analysis and decision-making processes. However, the process of choosing suitable ML algorithms for a specific task remains challenging, commonly referred to as the Algorithm Selection Problem (ASP) in academic literature. Automated machine learning (Auto-ML) tackles algorithm selection and hyperparameter adjustments by automating these processes, hence decreasing the required time and expertise. The current study is focused on the automated selection of noise filters for the task of classifying data with noisy labels. Class label noise, resulting from erroneous data labels, has a greater effect on the learning process compared to attribute-level noise. The existing Auto-ML approaches for selecting suitable noise filters overlook hyperparameter adjustment, which is important for the effectiveness of noise filters. Instead, it relies on computationally demanding techniques such as grid search. In addition, the current Auto-ML algorithms utilize random noise imputation techniques in their methodologies that are not 1 able to adequately capture the complex noise distributions typically present in real-world datasets. In order to overcome these limitations, we propose the introduction of Meta-learning for Noise-Filter Workflow Selection (MtL-NFW), an innovative framework that automate and integrate adequate hyperparameter selection along with noise filter for a certain task. This eliminates the need to rely on manual and computationally intensive methods for hyperparameters settings. Our second contribution is to leverage and employ the Neighbor-wise noise imputation technique to our framework, that induce noise based on proximity to class boundaries, generating more robust meta-training data. Our framework evaluates 114 classification datasets with six noise filtering algorithms and Random Forest as the base classifier, extracting six groups of meta-features and generating a meta-knowledge base of 51,642 configurations. XGBoost trained as meta-learner, predicts and recommends optimal noise filter and hyperparameter combinations for new datasets. Empirical validation on 114 OpenML datasets shows that MtL-NFW significantly improves the accuracy and robustness of noise filter recommendations, outperforming four state-of-the-art Auto-ML methods.

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Noise simulation in classification with the noisemodel R package: Applications analyzing the impact of errors with chemical data

Cited by 8 publications

References 71 publications

Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Differentiating Pressure Ulcer Risk Levels through Interpretable Classification Models Based on Readily Measurable Indicators

MtL-NFW: A Meta-Learning Framework for Automated Noise Filter Selection and Hyperparameter Optimization in Auto-ML

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