Multiple imputation with sequential penalized regression

Zahid, Faisal Maqbool; Heumann, Christian

doi:10.1177/0962280218755574

Cited by 17 publications

(8 citation statements)

References 39 publications

(52 reference statements)

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“…In the worst-case scenario, some attribute data cannot be collected at all. Missing data can be estimated through multiple imputations or predictions based on regression models [26]. The wrong classification probability p i→j is considered 197 with reference to categorical attributes.…”

Section: Covid-19)mentioning

confidence: 99%

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Sei

Ohsuga

2022

IEEE Access

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Available data may differ from true data in many cases due to sensing errors, especially for the Internet of Things (IoT). Although privacy-preserving data mining has been widely studied during the last decade, little attention has been paid to data values containing errors. Differential privacy, which is the de facto standard privacy metric, can be achieved by adding noise to a target value that must be protected. However, if the target value already contains errors, there is no reason to add extra noise. In this paper, a novel privacy model called true-value-based differential privacy (TDP) is proposed. This model applies traditional differential privacy to the "true value" unknown by the data owner or anonymizer but not to the "measured value" containing errors. Based on TDP, the amount of noise added by differential privacy techniques can be reduced by approximately 20% by our solution. As a result, the error of generated histograms can be reduced by 40.4% and 29.6% on average according to mean square error and Jensen-Shannon divergence, respectively.We validate this result on synthetic and five real data sets. Moreover, we proved that the privacy protection level does not decrease as long as the measurement error is not overestimated. INDEX TERMSData mining, Data privacy, Differentnial privacy, Internet of Things I. INTRODUCTION 1 Significant amounts of IoT data are generated every day by 2 many different sensors, such as thermal cameras, home appli-3 ance sensors, automotive sensors, and smartphone-equipped 4 sensors. These IoT data can be used for health monitoring [1], 5 context-aware recommendation (or recommender) systems 6 [2], navigation [3], and other applications.7 However, sensing people or their surrounding environment 8 might involve information that identifies an individual [4]. 9 Thus private information is at risk of leakage. By anonymiz-10 ing data based on ϵ-differential privacy [5], [6], which is the 11 de facto standard privacy metric (ϵ represents the privacy 12 budget), privacy leakage can be controlled. Differential pri-13 vacy has been used in many studies, such as [7]-[9], as it is 14 one of the most critical privacy metrics [10]. It is considered 15 an important concept for data analysis [11], [12]. 16 Local differential privacy is a specialized concept of dif-17 ferential privacy especially for data collection from each 18 person. In this paper, "differential privacy," refers to "lo-19

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Section: Covid-19)mentioning

confidence: 99%

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Sei

Ohsuga

2022

IEEE Access

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“…Perhaps the most problematic issue with deep learning is the inability to identify precisely how the algorithm has determined the outcome, known colloquially as 'black-boxing' [61]. Black-boxing is an especially significant limitation in the medical context due to the implications on patient safety and ability to prove clinical reasoning [61,62].…”

Section: Supervised Learningmentioning

confidence: 99%

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

et al. 2020

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Background: Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big data in combination with dramatically increased computing power has spurred renewed interest in this technology over the last two decades. Main body: Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a subdiscipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses. Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles. Conclusion: We now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods. Background Clinical epigenetics is a promising field of research. There is evidence that DNA methylation changes at cytosinephosphate-guanine (CpG) sites are associated with disease development [1-3]. Beyond genetic background, DNA methylation may additionally reflect environmental exposures and could improve diagnostic accuracy and prognostic prediction of certain diseases and be targetable by personalised therapy in the future [4, 5]. The current medical environment is characterised by collection of vast amounts of patient, hospital, and administrative data [6, 7], which makes traditional approaches to investigating these data individually less ideal. Machine learning (ML), however, is able to integrate large and complex data sets [8]. These data sources have the potential to enhance patient care and outcomes. A personalised medicine approach is tightly connected to increases in omics-data. For example, DNA sequence databases double in size twice a year [9]. Indeed, the increases in computer processing coupled with the rapid reduction in the cost of genomic sequencing have outpaced the rate of computing hardware advances [10]. Whilst far from a panacea, ML may be a tool to assist physicians in interpreting information-rich clinical data, including those collected in epigenetic studies [11, 12].

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“…By filling in the missing values with some reasonable values, imputation produces the complete data without eliminating the missing cases for analysis. Some ad-hoc methods, including mean substitution, maximum likelihood approaches, single imputation, and multiple imputation (MI), can be used to impute missing data [6]. Therefore, to overcome the missing values in high-dimensional data, reliable imputation approaches are required.…”

Section: Introductionmentioning

confidence: 99%

“…It has also become challenging to apply sequential regression imputation in this situation. [6], [24].…”

Section: Introductionmentioning

confidence: 99%

Improving Penalized Logistic Regression Model with Missing Values in High-Dimensional Data

2022

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Analysis without adequate handling of missing values may lead to inconsistent and biased estimates. Despite multiple imputations becoming a widely used approach in handling missing data, manuscript researchers generally encounter missing data in their respective studies. In high-dimensional data, penalized regression is a popular technique for performing feature selection and coefficient estimation simultaneously. However, one of the most vital issues with high-dimensional data is that it often contains large quantities of missing data that common multiple imputation approaches may not work correctly. Therefore, this study uses imputations penalized regression models as an extension of the penalized methods to improve the performance and impute missing values in high-dimensional data. The method was applied to real-life high-dimensional datasets for the different number of features, sample sizes, and missing dataset rates to evaluate its eﬃciency. The method was also compared with other existing imputation penalized methods for high-dimensional data. The comparative experimental results indicate that the proposed method outperforms its competitors by achieving higher sensitivity, specificity, and classification accuracy values.

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Multiple imputation with sequential penalized regression

Cited by 17 publications

References 39 publications

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

Improving Penalized Logistic Regression Model with Missing Values in High-Dimensional Data

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