Feature Selection and classification have previously been widely applied in various areas
like business, medical and media fields. High dimensionality in datasets is one of the main …

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 …

Feature selection has been widely applied in many areas such as classification of spam
emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray …

Feature selection aims at selecting important features that can enhance learning
performance in data mining, pattern recognition, and machine learning. Filter feature …

Adequate selection of features may improve accuracy and efficiency of classifier methods.
There are two main approaches for feature selection: wrapper methods, in which the …

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 …

Feature selection is frequently used to reduce the number of features in many applications
where data of high dimensionality are involved. Lots of the feature selection methods mainly …

Feature Selection Library (FSLib) is a widely applicable MATLAB library for Feature
Selection (FS). FS is an essential component of machine learning and data mining which …

Feature Selection for Machine Learning | Proceedings of the Twelfth International Florida
Artificial Intelligence Research Society Conference skip to main content ACM Digital Library …

Recently, large amount of data is widely available in information systems and data mining
has attracted a big attention to researchers to turn such data into useful knowledge. This …