Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study

BACKGROUND When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imputations by chained equations (MICE) as well as machine learning methods such as multilayer perceptron, k-nearest neighbor, and decision tree. OBJECTIVE The objective of this study was to impute numeric medical data such as physical data and laboratory data. We aimed to effectively impute data using a progressive method called self-training in the medical field where training data are scarce. METHODS In this paper, we propose a self-training method that gradually increases the available data. Models trained with complete data predict the missing values in incomplete data. Among the incomplete data, the data in which the missing value is validly predicted are incorporated into the complete data. Using the predicted value as the actual value is called pseudolabeling. This process is repeated until the condition is satisfied. The most important part of this process is how to evaluate the accuracy of pseudolabels. They can be evaluated by observing the effect of the pseudolabeled data on the performance of the model. RESULTS In self-training using random forest (RF), mean squared error was up to 12% lower than pure RF, and the Pearson correlation coefficient was 0.1% higher. This difference was confirmed statistically. In the Friedman test performed on MICE and RF, self-training showed a P value between .003 and .02. A Wilcoxon signed-rank test performed on the mean imputation showed the lowest possible P value, 3.05e-5, in all situations. CONCLUSIONS Self-training showed significant results in comparing the predicted values and actual values, but it needs to be verified in an actual machine learning system. And self-training has the potential to improve performance according to the pseudolabel evaluation method, which will be the main subject of our future research.

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Comparison of Algorithms for Clustering Incomplete Data

Foundations of Computing and Decision Sciences ◽

10.2478/fcds-2014-0007 ◽

2014 ◽

Vol 39 (2) ◽

pp. 107-127 ◽

Cited By ~ 6

Author(s):

Artur Matyja ◽

Krzysztof Siminski

Keyword(s):

Data Analysis ◽

Incomplete Data ◽

Missing Values ◽

Real Data ◽

Complete Data ◽

The Other ◽

Data Sets ◽

Missing Value ◽

Comparison Of Algorithms ◽

New Algorithms

Abstract The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

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Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider

Mathematics ◽

10.3390/math9040415 ◽

2021 ◽

Vol 9 (4) ◽

pp. 415

Author(s):

Yong-Chao Su ◽

Cheng-Yu Wu ◽

Cheng-Hong Yang ◽

Bo-Sheng Li ◽

Sin-Hua Moi ◽

...

Keyword(s):

Machine Learning ◽

Group Size ◽

Incomplete Data ◽

Field Data ◽

Ideal Free Distribution ◽

Rank Test ◽

Significant Feature ◽

P Value ◽

Data Imputation ◽

Spider Webs

Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k-nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p-value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam (Argyrodes miniaceus, Theridiidae) to test the occurrence and group size of kleptoparasites in natural populations. Our partial least-squares path modelling (PLS-PM) results demonstrated that the size of the host web (T = 6.890, p = 0.000) is a significant feature affecting group size. The resource size (T = 2.590, p = 0.010) and the microclimate (T = 3.230, p = 0.001) are significant features affecting the presence of kleptoparasites. The test of conformation of group size distribution to the ideal free distribution (IFD) model revealed that predictions pertaining to per-capita resource size were underestimated (bootstrap resampling mean slopes <IFD predicted slopes, p < 0.001). These findings highlight the importance of applying appropriate ML methods to the handling of missing field data.

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Evolutionary Machine Learning for Classification with Incomplete Data

10.26686/wgtn.17072123 ◽

2021 ◽

Author(s):

◽

Cao Truong Tran

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Genetic Programming ◽

Incomplete Data ◽

Missing Values ◽

Machine Learning Techniques ◽

Feature Construction ◽

Classification Algorithms ◽

Learning Techniques ◽

Effectiveness And Efficiency

Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors. Existing most researchers working on classification with incomplete data focused on improving the effectiveness, but did not adequately address the issue of the efficiency of applying the classifiers to classify unseen instances, which is much more important than the act of creating classifiers. A common approach to classification with incomplete data is to use imputation methods to replace missing values with plausible values before building classifiers and classifying unseen instances. This approach provides complete data which can be then used by any classification algorithm, but sophisticated imputation methods are usually computationally intensive, especially for the application process of classification. Another approach to classification with incomplete data is to build a classifier that can directly work with missing values. This approach does not require time for estimating missing values, but it often generates inaccurate and complex classifiers when faced with numerous missing values. A recent approach to classification with incomplete data which also avoids estimating missing values is to build a set of classifiers which then is used to select applicable classifiers for classifying unseen instances. However, this approach is also often inaccurate and takes a long time to find applicable classifiers when faced with numerous missing values. The overall goal of the thesis is to simultaneously improve the effectiveness and efficiency of classification with incomplete data by using evolutionary machine learning techniques for feature selection, clustering, ensemble learning, feature construction and constructing classifiers. The thesis develops approaches for improving imputation for classification with incomplete data by integrating clustering and feature selection with imputation. The approaches improve both the effectiveness and the efficiency of using imputation for classification with incomplete data. The thesis develops wrapper-based feature selection methods to improve input space for classification algorithms that are able to work directly with incomplete data. The methods not only improve the classification accuracy, but also reduce the complexity of classifiers able to work directly with incomplete data. The thesis develops a feature construction method to improve input space for classification algorithms with incomplete data by proposing interval genetic programming-genetic programming with a set of interval functions. The method improves the classification accuracy and reduces the complexity of classifiers. The thesis develops an ensemble approach to classification with incomplete data by integrating imputation, feature selection, and ensemble learning. The results show that the approach is more accurate, and faster than previous common methods for classification with incomplete data. The thesis develops interval genetic programming to directly evolve classifiers for incomplete data. The results show that classifiers generated by interval genetic programming can be more effective and efficient than classifiers generated the combination of imputation and traditional genetic programming. Interval genetic programming is also more effective than common classification algorithms able to work directly with incomplete data. In summary, the thesis develops a range of approaches for simultaneously improving the effectiveness and efficiency of classification with incomplete data by using a range of evolutionary machine learning techniques.

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Hematological profiles of visceral leishmaniasis patients before and after treatment of anti-leishmanial drugs at University of Gondar Hospital; Leishmania Research and Treatment Center Northwest, Ethiopia

BMC Infectious Diseases ◽

10.1186/s12879-021-06691-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elias Shiferaw ◽

Fadil Murad ◽

Mitikie Tigabie ◽

Mareye Abebaw ◽

Tadele Alemu ◽

...

Keyword(s):

Visceral Leishmaniasis ◽

Blood Cell ◽

Pearson Correlation ◽

Hematological Parameters ◽

Parasite Load ◽

Treatment Center ◽

Rank Test ◽

P Value ◽

Before And After ◽

The Mean

Abstract Background Visceral leshimaniasis is a parasitic disease characterized by systemic infection of phagocytic cells and an intense inflammatory response. The progression of the disease or treatment may have an effect on hematological parameters of these patients'. Thus, the current study sought to compare the hematological profiles of visceral leishmaniasis patients before and after treatment with anti-leishmaniasis drugs. Method An institutional-based retrospective cohort study was conducted among visceral leishmaniasis patients admitted to the University of Gondar comprehensive specialized referral hospital leishmaniasis research and treatment centre between September 2013 and August 2018. Hematological profiles were extracted from the laboratory registration book before and after treatment. Data were entered to Epi-info and exported to SPSS for analysis. Descriptive statistics were summarized using frequency and percentage to present with the table. The mean, standard deviation, median, and interquartile range were used to present the data. Furthermore, using the paired t-test and the Wilcoxon Signed rank test, the mean difference for normally and non-normally distributed data was compared. Spearman and Pearson correlation analysis were used to describe the relationship between hematological parameters and various variables. A P value of 0.05 was considered statistically significant. Result With the exception of the absolute neutrophil count, all post-treatment hematological parameters show a significant increase when compared to pre-treatment levels. Prior to treatment, the prevalence of anemia, leukopenia, and thrombocytopenia was 85.5, 83.4, and 75.8%, respectively, whereas it was 58.3, 38.2, and 19.2% following treatment. Furthermore, parasite load was found to have a statistically significant negative correlation with hematological profiles, specifically with white blood cell and red blood cell parameters. Conclusion According to our findings, patients with visceral leishmaniasis had improved hematological profiles after treatment. The effect of treatment on parasite proliferation and concentration within visceral organs, in which the parasite load could directly affect the patient's hematological profiles, may be associated with the change in hematological profiles.

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Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

10.1101/171967 ◽

2017 ◽

Cited By ~ 1

Author(s):

Runmin Wei ◽

Jingye Wang ◽

Mingming Su ◽

Erik Jia ◽

Tianlu Chen ◽

...

Keyword(s):

Mass Spectrometry ◽

Missing Values ◽

Pearson Correlation ◽

Imputation Accuracy ◽

Metabolomics Data ◽

Missing Value ◽

Sample Distribution ◽

Imputation Methods ◽

Missing Value Imputation ◽

Squared Error

AbstractIntroductionMissing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection of methods can significantly affect following data analyses and interpretations. According to the definition, there are three types of missing values, missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).ObjectivesThe aim of this study was to comprehensively compare common imputation methods for different types of missing values using two separate metabolomics data sets (977 and 198 serum samples respectively) to propose a strategy to deal with missing values in metabolomics studies.MethodsImputation methods included zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC). Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate the imputation accuracy for MCAR/MAR and MNAR correspondingly. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes sum of squared error were used to evaluate the overall sample distribution. Student’s t-test followed by Pearson correlation analysis was conducted to evaluate the effect of imputation on univariate statistical analysis.ResultsOur findings demonstrated that RF imputation performed the best for MCAR/MAR and QRILC was the favored one for MNAR.ConclusionCombining with “modified 80% rule”, we proposed a comprehensive strategy and developed a public-accessible web-tool for missing value imputation in metabolomics data.

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Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp2001-2013 ◽

2022 ◽

Vol 12 (2) ◽

pp. 2001

Author(s):

Jesmeen Mohd Zebaral Hoque ◽

Jakir Hossen ◽

Shohel Sayeed ◽

Chy. Mohammed Tawsif K. ◽

Jaya Ganesan ◽

...

Keyword(s):

Incomplete Data ◽

Missing Values ◽

Prediction Models ◽

Low Cost ◽

Support Vector ◽

Data Sampling ◽

Data Set ◽

Missing Value ◽

Missing Value Imputation ◽

Proper Analysis

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.

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MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS

Acta Polytechnica ◽

10.14311/ap.2021.61.0364 ◽

2021 ◽

Vol 61 (2) ◽

pp. 364-377

Author(s):

. Rustam ◽

Koredianto Usman ◽

Mudyawati Kamaruddin ◽

Dina Chamidah ◽

. Nopendri ◽

...

Keyword(s):

Experimental Data ◽

Incomplete Data ◽

Missing Values ◽

Complete Data ◽

Noise Sensitivity ◽

Data Sets ◽

Fuzzy C Means ◽

Number Of Iterations ◽

Fuzzy C Means Algorithm

A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM). However, the PFCM algorithm is only applicable to complete data sets. Therefore, this research modified the PFCM for clustering incomplete data sets to OCSPFCM and NPSPFCM with the performance evaluated based on three aspects, 1) accuracy percentage, 2) the number of iterations, and 3) centroid errors. The results showed that the NPSPFCM outperforms the OCSPFCM with missing values ranging from 5% − 30% for all experimental data sets. Furthermore, both algorithms provide average accuracies between 97.75%−78.98% and 98.86%−92.49%, respectively.

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AN APPLICATION OF GENETIC ALGORITHM FOR CLUSTERING OBSERVATIONS WITH INCOMPLETE DATA

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v1i1.48 ◽

2017 ◽

Vol 1 (1) ◽

pp. 13-23

Author(s):

Frisca Rizki Ananda ◽

Asep Saefuddin ◽

Bagus Sartono

Keyword(s):

Genetic Algorithm ◽

Cluster Analysis ◽

Incomplete Data ◽

Similarity Index ◽

Complete Data ◽

Missing Value ◽

Incomplete Observations ◽

Common Strategy ◽

Distance Approach ◽

Iris Data

Cluster analysis is a method to classify observations into several clusters. A common strategy for clustering the observations uses distance as a similarity index. However distance approach cannot be applied when data is not complete. Genetic Algorithm is applied by involving variance (GACV) in order to solve this problem. This study employed GACV on Iris data that was introduced by Sir Ronald Fisher. Clustering the incomplete data was implemented on data which was produced by deleting some values of Iris data. The algorithm was developed under R 3.0.2 software and got satisfying result for clustering complete data with 95.99% sensitivity and 98% consistency. GACV could be applied to cluster observations with missing value without filling in the missing value or excluding these observations. Performance on clustering incomplete observations is also satisfying but tends to decrease as the proportion of incomplete values increases. The proportion of incomplete values should be less than or equal to 40% to get sensitivity and consistency not less than 90. Keywords: Cluster Analysis, Genetic Algorithm, Incomplete Data.

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Evolutionary Machine Learning for Classification with Incomplete Data

10.26686/wgtn.17072123.v1 ◽

2021 ◽

Author(s):

◽

Cao Truong Tran

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Genetic Programming ◽

Incomplete Data ◽

Missing Values ◽

Machine Learning Techniques ◽

Feature Construction ◽

Classification Algorithms ◽

Learning Techniques ◽

Effectiveness And Efficiency

Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors. Existing most researchers working on classification with incomplete data focused on improving the effectiveness, but did not adequately address the issue of the efficiency of applying the classifiers to classify unseen instances, which is much more important than the act of creating classifiers. A common approach to classification with incomplete data is to use imputation methods to replace missing values with plausible values before building classifiers and classifying unseen instances. This approach provides complete data which can be then used by any classification algorithm, but sophisticated imputation methods are usually computationally intensive, especially for the application process of classification. Another approach to classification with incomplete data is to build a classifier that can directly work with missing values. This approach does not require time for estimating missing values, but it often generates inaccurate and complex classifiers when faced with numerous missing values. A recent approach to classification with incomplete data which also avoids estimating missing values is to build a set of classifiers which then is used to select applicable classifiers for classifying unseen instances. However, this approach is also often inaccurate and takes a long time to find applicable classifiers when faced with numerous missing values. The overall goal of the thesis is to simultaneously improve the effectiveness and efficiency of classification with incomplete data by using evolutionary machine learning techniques for feature selection, clustering, ensemble learning, feature construction and constructing classifiers. The thesis develops approaches for improving imputation for classification with incomplete data by integrating clustering and feature selection with imputation. The approaches improve both the effectiveness and the efficiency of using imputation for classification with incomplete data. The thesis develops wrapper-based feature selection methods to improve input space for classification algorithms that are able to work directly with incomplete data. The methods not only improve the classification accuracy, but also reduce the complexity of classifiers able to work directly with incomplete data. The thesis develops a feature construction method to improve input space for classification algorithms with incomplete data by proposing interval genetic programming-genetic programming with a set of interval functions. The method improves the classification accuracy and reduces the complexity of classifiers. The thesis develops an ensemble approach to classification with incomplete data by integrating imputation, feature selection, and ensemble learning. The results show that the approach is more accurate, and faster than previous common methods for classification with incomplete data. The thesis develops interval genetic programming to directly evolve classifiers for incomplete data. The results show that classifiers generated by interval genetic programming can be more effective and efficient than classifiers generated the combination of imputation and traditional genetic programming. Interval genetic programming is also more effective than common classification algorithms able to work directly with incomplete data. In summary, the thesis develops a range of approaches for simultaneously improving the effectiveness and efficiency of classification with incomplete data by using a range of evolutionary machine learning techniques.

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