An Empirical Study of Dynamic Incomplete-Case Nearest Neighbor Imputation in Software Quality Data

International audienceSoftware quality prediction is an important yet difficult problem in software project development and management. Historical datasets can be used to build models for software quality prediction. However, the missing data significantly affects the prediction ability of models in knowledge discovery. Instead of ignoring missing observations, we investigate and improve incomplete-case k-nearest neighbor based imputation. K-nearest neighbor imputation is widely applied but has rarely been improved to have the most appropriate parameter settings for each imputation. This work conducts imputation on four well-known software quality datasets to discover the impact of the new imputation method we proposed. We compare it with mean imputation and other commonly used versions of k-nearest neighbor imputation. The empirical results show that the proposed dynamic incomplete-case nearest neighbor imputation performs better when the missingness is completely at random or non-ignorable, regardless of the percentage of missing valu

Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition

Over the last decade, research has focused on machine learning and data mining to develop frameworks that can improve data analysis and output performance; to build accurate decision support systems that benefit from real-life datasets. This leads to the field of clinical data analysis, which has attracted a significant amount of interest in the computing, information systems, and medical fields. To create and develop models by machine learning algorithms, there is a need for a particular type of data for the existing algorithms to build an efficient model. Clinical datasets pose several issues that can affect the classification of the dataset: missing values, high dimensionality, and class imbalance. In order to build a framework for mining the data, it is necessary first to preprocess data, by eliminating patients’ records that have too many missing values, imputing missing values, addressing high dimensionality, and classifying the data for decision support.This thesis investigates a real clinical dataset to solve their challenges. Autoencoder is employed as a tool that can compress data mining methodology, by extracting features and classifying data in one model. The first step in data mining methodology is to impute missing values, so several imputation methods are analysed and employed. Then high dimensionality is demonstrated and used to discard irrelevant and redundant features, in order to improve prediction accuracy and reduce computational complexity. Class imbalance is manipulated to investigate the effect on feature selection algorithms and classification algorithms.The first stage of analysis is to investigate the role of the missing values. Results found that techniques based on class separation will outperform other techniques in predictive ability. The next stage is to investigate the high dimensionality and a class imbalance. However it was found a small set of features that can improve the classification performance, the balancing class does not affect the performance as much as imbalance class

Predictability of Missing Data Theory to Improve U.S. Estimator’s Unreliable Data Problem

Since the topic of improving data quality has not been addressed for the U.S. defense cost estimating discipline beyond changes in public policy, the goal of the study was to close this gap and provide empirical evidence that supports expanding options to improve software cost estimation data matrices for U.S. defense cost estimators. The purpose of this quantitative study was to test and measure the level of predictive accuracy of missing data theory techniques that were referenced as traditional approaches in the literature, compare each theories’ results to a complete data matrix used in support of the U.S. defense cost estimation discipline, and determine which theories rendered incomplete and missing data sets in a single data matrix most reliable and complete under eight missing value percentages. A quantitative pre-experimental research design, a one group pretest-posttest no control group design, empirically tested and measured the predictive accuracy of traditional missing data theory techniques typically used in non-cost estimating disciplines. The results from the pre-experiments on a representative U.S. defense software cost estimation data matrix obtained, a nonproprietary set of historical software effort, size, and schedule numerical data used at Defense Acquisition University revealed that single and multiple imputation techniques were two viable options to improve data quality since calculations fell within 20% of the original data value 16.4% and 18.6%, respectively. This study supports positive social change by investigating how cost estimators, engineering economists, and engineering managers could improve the reliability of their estimate forecasts, provide better estimate predictions, and ultimately reduce taxpayer funds that are spent to fund defense acquisition cost overruns