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A transfer learning environment is characterized by a machine learning algorithm being trained with data from one domain (the source domain) and being tested on data from a different domain (the target domain). In a transfer learning scenario, the class probability of the source domain may be different from the class probability of the target domain, which is referred to as "domain class imbalance"...
Boosting methods have been successfully applied in a wide variety of machine learning applications. In the context of data quality issues, a number of variants of the standard boosting method have been proposed and evaluated. To address the problem of mislabeled examples, ORBoost was developed to prevent over fitting to noisy examples. Our research group has recently proposed RUSBoost as an enhancement...
High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that in-corporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group...
Software metric (feature) selection is an important pre-processing step before building software defect prediction models. Although much research has been done analyzing the classification performance of feature selection methods, fewer works have focused on their stability (robustness). Stability is important because feature selection methods which reliably produce the same results despite changes...
Feature selection has been applied in many domains, such as text mining and software engineering. Ideally a feature selection technique should produce consistent outputs regardless of minor variations in the input data. Researchers have recently begun to examine the stability (robustness) of feature selection techniques. The stability of a feature selection method is defined as the degree of agreement...
Software defect prediction can be considered a binary classification problem. Generally, practitioners utilize historical software data, including metric and fault data collected during the software development process, to build a classification model and then employ this model to predict new program modules as either fault-prone (fp) or not-fault-prone (nfp). Limited project resources can then be...
Feature Selection is a process which identifies irrelevant and redundant features from a high-dimensional dataset (that is, a dataset with many features), and removes these before further analysis is performed. Recently, the robustness (e.g., stability) of feature selection techniques has been studied, to examine the sensitivity of these techniques to changes in their input data. In this study, we...
Reducing the number of latent software defects is a development goal that is particularly applicable to high assurance software systems. For such systems, the software measurement and defect data is highly skewed toward the not-fault-prone program modules, i.e., the number of fault-prone modules is relatively very small. The skewed data problem, also known as class imbalance, poses a unique challenge...
Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers...
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