Feature selection is the task of selecting a small subset from original features that can
achieve maximum classification accuracy. This subset of features has some very important …

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 is an important filtering method for data analysis, pattern classification,
data mining, and so on. Feature selection reduces the number of features by removing …

In this paper, we examine the advantages and disadvantages of filter and wrapper methods
for feature selection and propose a new hybrid algorithm that uses boosting and …

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-------Feature selection is considered as a problem of global combinatorial optimization in …

The presence of a large number of irrelevant features degrades the classifier accuracy,
reduces the understanding of data, and increases the overall time needed for training and …

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 …

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

Today, feature selection is an active research in machine learning. The main idea of feature
selection is to choose a subset of available features, by eliminating features with little or no …

This paper introduces a wrapper method, namely cosine similarity measure support vector
machines (CSMSVM), to eliminate irrelevant or redundant features during classifier …