Lofton A. Bullard

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The value of knowledge inferred from information databases is critically dependent on the quality of data. We present multiple imputation as a reliable and consistent imputation technique for handling missing data in a numeric dependent variable in software metrics data sets. Experiments were conducted using multiple, mean, k-Nearest Neighbors, regression,(More)
This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was utilized for this study because of its(More)
The detrimental effects of noise in a dependent variable on the accuracy of software quality imputation techniques were studied. The imputation techniques used in this work were Bayesian multiple imputation, mean imputation, instance-based learning, regression imputation, and the REPTree decision tree. These techniques were used to obtain software quality(More)
A new rule-based classification model (RBCM) and rule-based model selection technique are presented. The RBCM utilizes rough set theory to significantly reduce the number of attributes, discretation to partition the domain of attribute values, and Boolean predicates to generate the decision rules that comprise the model. When the domain values of an(More)
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