Feature selection is an important step in data mining and is used in various domains
including genetics, medicine, and bioinformatics. Choosing the important features (genes) is …

Selecting a subset of relevant features is crucial to the analysis of high-dimensional datasets
coming from a number of application domains, such as biomedical data, document and …

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 …

Finding a good predictive model for a high‐dimensional data set can be challenging. For
genetic data, it is not only important to find a model with high predictive accuracy, but it is …

Feature selection has become a necessary step to the analysis of high-dimensional datasets
coming from several application domains (eg, web data, document and image analysis …

Feature selection is one of the most fundamental problems in data analysis, machine
learning, and data mining. Recently, it has drawn increasing attention due to high …

Our study aims to investigate the stability and the selection accuracy of feature selection
performance under different data complexity. The motivation behind this investigation is that …

Software quality modeling is the process of using software metrics from previous iterations of
development to locate potentially faulty modules in current under-development code. This …

With the explosive growth of high-dimensional data, feature selection has become a crucial
step of machine learning tasks. Though most of the available works focus on devising …

One of the major challenges when working with software metrics datasets is that some
metrics may be redundant or irrelevant to software defect prediction. This may be addressed …