Feature Selection for Machine Learning | Proceedings of the Twelfth International Florida
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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 …

Recent work has shown that feature subset selection can have a position affect on the
performance of machine learning algorithms. Some algorithms can be slowed or their …

A central problem in machine learning is identifying a representative set of features from
which to construct a classification model for a particular task. This thesis addresses the …

In Wrapper based feature selection, the more states that are visited during the search phase
of the algorithm the greater the likelihood of finding a feature subset that has a high internal …

Feature selection is an integral step of the data mining process to find an optimal subset of
features. After examining the problems with both the filter and the wrapper approach to …

It is generally accepted that Wrapper approaches will outperform Filter-based approaches to
feature selection, particularly in situations where an adequate amount of data is available …

Feature selection is an important part of classifier design. There are many possible methods
for searching and evaluating feature subsets, but little consensus on which methods are …

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

In the wrapper approach to feature subset selection, a search for an optimal set of features is
made using the induction algorithm as a black box. The estimated future performance of the …