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With the proliferation of high-dimensional datasets across many application domains in recent years, feature selection has become an important data mining task due to its capability to improve both performance and computational efficiencies. The chosen feature subset is important not only due to its ability to improve classification performance, but also because in some domains, knowing the most important...
Dimensionality-reducing techniques such as gene selection have become commonplace in order to reduce the high dimensionality found within bioinformatics datasets such as DNA microarray datasets. The degree of dimensionality is reduced by identifying and removing redundant and irrelevant features or genes and leaving only an optimum subset of features for subsequent analysis. However, a number of feature...
Ensemble feature selection has recently become a topic of interest for researchers, especially in the area of bioinformatics. The benefits of ensemble feature selection include increased feature (gene) subset stability and usefulness as well as comparable (or better) classification performance compared to using a single feature selection method. However, existing work on ensemble feature selection...
In software quality modeling, software metrics are collected during the software development cycle. However, not all metrics are relevant to the class attribute (software quality). Metric (feature) selection has become the cornerstone of many software quality classification problems. Selecting software metrics that are important for software quality classification is a necessary and critical step...
Feature selection is an important preprocessing step when learning from bioinformatics datasets. Since these datasets often have high dimensionality (a large number of features), selecting the most important ones both improves performance and reduces computation time. In addition, when the features in question are genes (as is the case for microarray datasets), knowing the important genes is useful...
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...
This paper presents a noise-based stability performance evaluation approach for feature selection techniques. For the stability assessment, a similarity-based measure is used to quantify the degree of agreement between a filter's output on a clean dataset and its outputs on the same dataset corrupted with different combinations of noise level and noise distribution. Experiments are conducted with...
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...
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