Feature Selection (FS) has been an active research area in Pattern Recognition, Statistics,
and Data Mining Techniques. FS is a process to select most instructive features from the …

Computational models of the artificial intelligence such as soft set theory have several
applications. Soft data reduction can be considered as a machine learning technique for …

Clustering, which is a set of categorical data into a homogenous class, is a fundamental
operation in data mining. One of the techniques of data clustering was performed by …

Clustering is one of the most useful tasks in data mining process for discovering groups and
identifying interesting distributions and patterns in the underlying data. One of the …

Big data is a collection of larger volume of unstructured types of data such as images, videos
and social media data. Recently, the applications of big data analytics in the field of medical …

Lung cancer is the deadliest type of cancer for both men and women. Feature selection
plays a vital role in cancer classification. This paper investigates the feature selection …

Binary relations between alternatives and parameters in a soft set have a vital role in the
analysis of configuration of this soft set. All of the studies on the soft set approach focus on …

Decision-making involves several processes such as data pre-processing, data reduction
and data selection. In order to assure a valuable solution is made, each of these processes …

Existing classification techniques, which are previously proposed for eliminating data
inconsistency, could not achieve an efficient parameter reduction in soft set theory as it …

This paper presents the applicability of soft set theory for discovering a decision attribute in
information systems. It is based on the notion of a mapping inclusion in soft set theory. The …