Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Chlioui, Imane; Abnane, Ibtissam; Idri, Ali

doi:10.1007/978-3-030-58811-3_5

Cited by 4 publications

(2 citation statements)

References 51 publications

(33 reference statements)

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“…In this study, we utilized DT with the CART algorithm, which demonstrated high accuracy and stable performance due to the absence of marked non-homogeneous characteristics in the random simulation data. Relevant studies 11,12,26 also demonstrated that the above three imputation methods had excellent performance when applied to datasets with continuous and mixed variables. Our study supported these findings from the perspective of missing dichotomous variables.…”

Section: Discussionmentioning

confidence: 94%

A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods

Zhang

2023

Sci Rep

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The problem of missing data, particularly for dichotomous variables, is a common issue in medical research. However, few studies have focused on the imputation methods of dichotomous data and their performance, as well as the applicability of these imputation methods and the factors that may affect their performance. In the arrangement of application scenarios, different missing mechanisms, sample sizes, missing rates, the correlation between variables, value distributions, and the number of missing variables were considered. We used data simulation techniques to establish a variety of different compound scenarios for missing dichotomous variables and conducted real-data validation on two real-world medical datasets. We comprehensively compared the performance of eight imputation methods (mode, logistic regression (LogReg), multiple imputation (MI), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN)) in each scenario. Accuracy and mean absolute error (MAE) were applied to evaluating their performance. The results showed that missing mechanisms, value distributions and the correlation between variables were the main factors affecting the performance of imputation methods. Machine learning-based methods, especially SVM, ANN, and DT, achieved relatively high accuracy with stable performance and were of potential applicability. Researchers should explore the correlation between variables and their distribution pattern in advance and prioritize machine learning-based methods for practical applications when encountering dichotomous missing data.

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Section: Discussionmentioning

confidence: 94%

A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods

Zhang

2023

Sci Rep

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“…Step 4: Model comparison using the SK test The SK test [32] allows comparing several models in terms of performance in order to conclude the existence of a significant difference between them [31,[33][34][35]. The comparison was performed using the MCC criterion.…”

Section: Methodsmentioning

confidence: 99%

Impact of Recursive Feature Elimination with Cross-validation in Modeling the Spatial Distribution of Three Mosquito Species in Morocco

Douider¹,

Amrani²,

Balenghien³

et al. 2022

RIA

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Many studies in ecology are interested in characterizing the ecological factors; determining the distribution of animal species. The classical approach consists in identifying the combination of ecological factors that allow reproducing observations of the presence and absence of the species of interest. The major difficulty lies in the imbalance between a considerable quantity of ecological factors to be tested and a relatively limited number of presence/absence observations. Selection of the most influential ecological features is a classical data pre-processing strategy that aims to overcome this imbalance and improve model performance. In this paper, we applied recursive feature elimination with cross-validation (RFECV) approach on presence/absence mosquito data in Morocco; to select optimal subsets of ecological features, in order to improve the performance of the predictive models. This method demonstrated the best ability to improve the performance of the predictive models, and can be recommended as a modeling improvement technique for large datasets.

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Missing data imputation using correlation coefficient and min-max normalization weighting

Shantal,

Othman,

Abu Bakar

2024

IDA

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Missing data is one of the challenges a researcher encounters while attempting to draw information from data. The first step in solving this issue is to have the data stage ready for processing. Much effort has been made in this area; removing instances with missing data is a popular method for handling missing data, but it has drawbacks, including bias. It will be impacted negatively on the results. How missing values are handled depends on several vectors, including data types, missing rates, and missing mechanisms. It covers missing data patterns as well as missing at random, missing at completely random, and missing not at random. Other suggestions include using numerous imputation techniques divided into various categories, such as statistical and machine learning methods. One strategy to improve a model’s output is to weight the feature values to better the performance of classification or regression approaches. This research developed a new imputation technique called correlation coefficient min-max weighted imputation (CCMMWI). It combines the correlation coefficient and min-max normalization techniques to balance the feature values. The proposed technique seeks to increase the contribution of features by considering how those elements relate to the desired functionality. We evaluated several established techniques to assess the findings, including statistical techniques, mean and EM imputation, and machine learning imputation techniques, including k-NNI, and MICE. The evaluation also used the imputation techniques CBRL, CBRC, and ExtraImpute. We use various sizes of datasets, missing rates, and random patterns. To compare the imputed datasets and original data, we finally provide the findings and assess them using the root mean squared error (RMSE), mean absolute error (MAE), and R2. According to the findings, the proposed CCMMWI performs better than most other solutions in practically all missing-rate scenarios.

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Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Cited by 4 publications

References 51 publications

A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods

A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods

Impact of Recursive Feature Elimination with Cross-validation in Modeling the Spatial Distribution of Three Mosquito Species in Morocco

Missing data imputation using correlation coefficient and min-max normalization weighting

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