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The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset. [ABSTRACT FROM AUTHOR]

Ohio University has been conducting research on first-year-student retention to prevent dropouts (OU Office of Institutional Research, First-Year Students Retention, 2008). Yet, the data set has more than 20% missing values. Missing data affects the ability in result generalization of the target population. This study categorizes the missing data into one of three types of missing data: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). After the missing data is identified, the proper method of handling the data is discussed. Five methods were utilized in the research: mean, median, zero, hot-deck and multiple imputations. Despite the poor performance on the accuracy comparison test, multiple and hot-deck imputation have proven to improve the retention prediction rate. Mean and median imputation perform better in accuracy and are sufficient for the prediction model. [ABSTRACT FROM AUTHOR]

Cisco Systems, Inc., conducts a customer satisfaction survey (CSAT) each year to gauge customer sentiment regarding Cisco products, technical support, partner- and Cisco-provided technical services, order fulfillment, and a number of other aspects of the companys business. The results of the analysis of this data are used for several purposes, including ascertaining the viability of new products, determining if customer support objectives are being met, setting engineering in-process and customer experience yearly metrics goals, and assessing, indirectly, the success of engineering initiatives. Analyzing this data, which includes 110,000 yearly sets of survey responses that address over 100 product and services categories, is in many respects complicated. For example, skip logic is an integral part of the survey mechanics, and forming aggregate views of customer sentiment is statistically challenging in this data environment. In this paper, we describe several of the various analysis approaches currently used, pointing out some situations where a high level of precision is not easily achieved, and some situations in which it is possible to easily end up with erroneous results. The analysis and statistical territory covered in this paper is in parts well-known and straightforward, but other parts, which we address, are susceptible to large inaccuracies and errors. We address several of these difficulties and develop reasonable solutions for two known issues, high missing value levels and high colinearity of independent variables. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to investigate the impact of missing data and data imputation on the data mining phase of knowledge discovery when neural networks employing an s-Sigmoid Transfer function are utilized. While studies have been conducted independently in the areas knowledge discovery, missing data and data imputation, only a few have integrated all three dimensions. This research explores the impact of data missingness at various increasing levels in KDD (knowledge discovery databases) models that contain various volumes of case frequencies that employ neural networks as the data mining algorithm. Four of the most commonly utilized data imputation methods - Case Deletion, Mean Substitution, Regression Imputation, and Multiple Imputation, are used to determine their effectiveness in dealing with the issue of data missingness. [ABSTRACT FROM AUTHOR]

Item nonresponse for income and wealth questions is a persistent problem for survey research. Estimates of the level of item nonresponse for income questions runs as high as 25 percent. This paper applies a test suggested by (sherman 2000) for `ignorability' of missing item data, using an interviewer estimate of socio-economic status. Missing household income data is found to be independent of SES. Some implications for the data imputation of survey income item nonresponse are discussed. [ABSTRACT FROM AUTHOR]