Generating Synthetic Missing Data: A Review by Missing Mechanism <s> I. INTRODUCTION <s> Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values. <s> BIB001 </s> Generating Synthetic Missing Data: A Review by Missing Mechanism <s> I. INTRODUCTION <s> Breast Cancer is the most common type of cancer in women worldwide. In spite of this fact, there are insufficient studies that, using data mining techniques, are capable of helping medical doctors in their daily practice. <s> BIB002 </s> Generating Synthetic Missing Data: A Review by Missing Mechanism <s> I. INTRODUCTION <s> D, Center for ResearchMethods and Data Analysis, University of Kansas, Lawrence, KS 66045, USA. E-mail: yhat@ku.eduReceived February 28, 2013; revisions received and accepted May 30, 2013We provide conceptual introductions to missingness mechanisms—missing completely at random, missing atrandom, and missing not at random—and state-of-the-art methods of handling missing data—full-informa-tion maximum likelihood and multiple imputation—followed by a discussion of planned missing designs:Multiform questionnaire protocols, 2-method measurement models, and wave-missing longitudinal designs.We reviewed 80 articles of empirical studies published in the 2012 issues of the Journal of PediatricPsychology to present a picture of how adequately missing data are currently handled in this field. Toillustrate the benefits of using multiple imputation or full-information maximum likelihood and incorporatingplanned missingness into study designs, we provide example analyses of empirical data gathered using a3-form planned missing design.Key words full-information maximum likelihood; missing data analysis; missingness mechanisms; multipleimputation; planned missing design.Missing data are not problematic, per se—how weapproach and treat missing data, on the other hand, canbe highly problematic. In fact, all of the traditionalmethods that are still popular in pediatric research sufferfrom many drawbacks that introduce bias and lack ofpower (Enders, 2010; Graham, 2012; van Buuren,2011). In contrast to the drawbacks of traditional app-roaches (e.g., listwise or pairwise deletion, mean substitu-tion), modern treatments for missing data such as multipleimputation (MI) and full-information maximum likelihood(FIML) easily accommodate valid inferences when thestudy design includes careful consideration of the reasonsfor why data might go missing. We provide a conceptualintroduction to the steadily growing literature on modernmissing data treatments. Admittedly, the devil is in thedetails of how to best minimize any deleterious effects ofmissing data, and we provide citations to ample resourceswhere these details are carefully described. In this article,therefore, we only highlight the many ways that missingdata can and should be addressed in pediatric research.When a sample of participants is used to make infer-ences about a population of persons, three issues arisewhen data are missing: power, bias, and recoverability.How much power has been lost? Are the remaining datanow a biased representation of the population? Can thepower loss be recovered? Can the bias be recovered?With modern treatments for missing data, the answer toeach of these questions depends on which of three possiblemechanisms (Rubin, 1976) is responsible for the missingdata. All missing data happen because of one of these threemechanisms, and in any data collection endeavor, all threemechanisms are likely to be involved to some degree. <s> BIB003 </s> Generating Synthetic Missing Data: A Review by Missing Mechanism <s> I. INTRODUCTION <s> Display Omitted Survival prediction of HCC patients is modeled in a real and high complex scenario.Its complexity is due to a small dataset with heterogeneous and missing input data.We propose a new cluster-based oversampling method to overcome these drawbacks.Neural networks and logistic regression are applied in the classification stage.Experimental results show that our method outperforms previous existing approaches. Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess each patients treatment on the basis of evidence-based medicine, which may not always apply to a specific patient, given the biological variability among individuals. Over the years, and for the particular case of Hepatocellular Carcinoma, some research studies have been developing strategies for assisting clinicians in decision making, using computational methods (e.g. machine learning techniques) to extract knowledge from the clinical data. However, these studies have some limitations that have not yet been addressed: some do not focus entirely on Hepatocellular Carcinoma patients, others have strict application boundaries, and none considers the heterogeneity between patients nor the presence of missing data, a common drawback in healthcare contexts. In this work, a real complex Hepatocellular Carcinoma database composed of heterogeneous clinical features is studied. We propose a new cluster-based oversampling approach robust to small and imbalanced datasets, which accounts for the heterogeneity of patients with Hepatocellular Carcinoma. The preprocessing procedures of this work are based on data imputation considering appropriate distance metrics for both heterogeneous and missing data (HEOM) and clustering studies to assess the underlying patient groups in the studied dataset (K-means). The final approach is applied in order to diminish the impact of underlying patient profiles with reduced sizes on survival prediction. It is based on K-means clustering and the SMOTE algorithm to build a representative dataset and use it as training example for different machine learning procedures (logistic regression and neural networks). The results are evaluated in terms of survival prediction and compared across baseline approaches that do not consider clustering and/or oversampling using the Friedman rank test. Our proposed methodology coupled with neural networks outperformed all others, suggesting an improvement over the classical approaches currently used in Hepatocellular Carcinoma prediction models. <s> BIB004 </s> Generating Synthetic Missing Data: A Review by Missing Mechanism <s> I. INTRODUCTION <s> Breast cancer is the most frequently diagnosed cancer in women. Using historical patient information stored in clinical datasets, data mining and machine learning approaches can be applied to predict the survival of breast cancer patients. A common drawback is the absence of information, i.e., missing data, in certain clinical trials. However, most standard prediction methods are not able to handle incomplete samples and, then, missing data imputation is a widely applied approach for solving this inconvenience. Therefore, and taking into account the characteristics of each breast cancer dataset, it is required to perform a detailed analysis to determine the most appropriate imputation and prediction methods in each clinical environment. This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information (most clinical data of the patients are incomplete), which is a challenge in terms of complexity. Four scenarios are evaluated: (I) 5-year survival prediction without imputation and 5-year survival prediction from cleaned dataset with (II) Mode imputation, (III) Expectation-Maximization imputation and (IV) K-Nearest Neighbors imputation. Prediction models for breast cancer survivability are constructed using four different methods: K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines. Experiments are performed in a nested ten-fold cross-validation procedure and, according to the obtained results, the best results are provided by the K-Nearest Neighbors algorithm: more than 81% of accuracy and more than 0.78 of area under the Receiver Operator Characteristic curve, which constitutes very good results in this complex scenario. HighlightsA 5-year survival model is constructed in a real breast cancer context.The dataset is a challenge in terms of complexity due to its high missing data ratio.Several representative imputation and decision methods are analyzed.Obtained results are very interesting and accurate for this complex clinical dataset. <s> BIB005 </s> Generating Synthetic Missing Data: A Review by Missing Mechanism <s> I. INTRODUCTION <s> Dealing with missing data is a crucial step in the preprocessing stage of most data mining projects. Especially in healthcare contexts, addressing this issue is fundamental, since it may result in keeping or loosing critical patient information that can help physicians in their daily clinical practice. Over the years, many researchers have addressed this problem, basing their approach on the implementation of a set of imputation techniques and evaluating their performance in classification tasks. These classic approaches, however, do not consider some intrinsic data information that could be related to the performance of those algorithms, such as features’ distribution. Establishing a correspondence between data distribution and the most proper imputation method avoids the need of repeatedly testing a large set of methods, since it provides a heuristic on the best choice for each feature in the study. The goal of this work is to understand the relationship between data distribution and the performance of well-known imputation techniques, such as Mean, Decision Trees, k-Nearest Neighbours, Self-Organizing Maps and Support Vector Machines imputation. Several publicly available datasets, all complete, were selected attending to several characteristics such as number of distributions, features and instances. Missing values were artificially generated at different percentages and the imputation methods were evaluated in terms of Predictive and Distributional Accuracy. Our findings show that there is a relationship between features’ distribution and algorithms’ performance, although some factors must be taken into account, such as the number of features per distribution and the missing rate at state. <s> BIB006
Missing Data (MD) consists of the existence of absent observations (values) in data and is a common obstacle researchers face in real-world contexts BIB002 - BIB004 . MD occurs in a variety of domains, for several different reasons, and regardless of whatever they might be, has serious implications for knowledge extraction and classification performance. When datasets are incomplete, pattern classification turns into a more complex task; therefore, over the years, researchers have invested in developing effective strategies to replace the missing values by plausible substitute values, a process generally designated by data imputation BIB003 . A classical approach to data imputation studies follows 4 mains steps ( Figure 1 ): 1) Collection of several complete datasets to perform the experiments. Depending on the nature of the domain, these datasets may encompass several feature types (e.g. qualitative/quantitative) and different dimensionality (number of features and number of patterns); 2) Synthetic generation of missing data. Missing values can be generated in only one feature (univariate configuration) or several features (multivariate configuration), at several percentages (missing rates). Furthermore, the generation may follow 3 different underlying mechanisms: Missing Completely At Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR)  ; 3) Data imputation using several strategies: common choices rely on statistical-based methods (e.g. mean/ mode imputation) or machine learning-based methods (e.g. KNN imputation) BIB001 ; 4) Evaluation of imputation algorithms, either in terms of classification performance (e.g. AUC values) BIB005 or quality of imputation (e.g. RMSE values) BIB006 , by comparing the substitute values with the ground truth (known original values).