Data from many real-world applications can be high dimensional and features of such data
are usually highly redundant. Identifying informative features has become an important step …

Dominance-based rough approximation discovers inconsistencies from ordered criteria and
satisfies the requirement of the dominance principle between single-valued domains of …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Linear discriminant analysis (LDA) is a very popular supervised feature extraction method
and has been extended to different variants. However, classical LDA has the following …

Feature selection is a multi-objective optimization problem since it has two conflicting
objectives: maximizing the classification accuracy and minimizing the number of the …

Feature selection is a complex optimization problem with important real-world applications.
Normally, its main target is to reduce the dimensionality of the dataset and increase the …

Feature selection is a technique commonly used in Data Mining and Machine Learning.
Traditional feature selection methods, when applied to large datasets, generate a large …

Feature selection is an important component of the machine learning domain, which selects
the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a …

Feature selection is a necessary critical stage in data mining process. There is always an
arm race to build frameworks and libraries that ease and automate this process. In this …

Fuzzy rough set theory can tackle feature redundancy in data and select more informative
features for machine learning tasks. Gaussian kernel is often coupled with fuzzy rough set …