Feature selection is one of the most important preprocessing steps in data mining and
knowledge Engineering. In this short review paper, apart from a brief taxonomy of current …

Objectives: This study summarizes the feature selection process, its importance, different
types of feature selection algorithms such as Filter, Wrapper and Hybrid. Moreover, it …

Feature selection, the process of identifying relevant features to be incorporated into a
proposed model, is one of the significant steps of the learning process. It removes noise from …

Identifying the relevant set of features in a dataset is an important part of data analytics.
Discarding significant variables or keeping irrelevant variables has significant effects on the …

Dimensionality (ie, the number of data set attributes or groups of attributes) constitutes a
serious obstacle to the efficiency of most data mining algorithms (Maimon and Last, 2000) …

Feature selection has been the focus of interest for quite some time and much work has
been done. With the creation of huge databases and the consequent requirements for good …

One of the major aspects of any classification process is selecting the relevant set of features
to be used in a classification algorithm. This initial step in data analysis is called the feature …

One of the challenges in data mining is the dimensionality of data, which is often very high
and prevalent in many domains, such as text categorization and bio-informatics. The high …

Data mining is an inevitable step in knowledge discovery and it helps discovering hidden
and useful patterns among data. These days, the number of stored attributes for each entity …

Feature selection is the process of eliminating features from the data set that are irrelevant
with respect to the task to be performed. Feature selection is important for many reasons …