A Conception of Feature Selection Algorithms in Data Mining |
Author(s): |
| V. Rengaraj , Thanthai Hans Roever College, Perambalur; S. Firdhouse, Thanthai Hans Roever College, Perambalur |
Keywords: |
| Feature selection algorithm, Euclidian distance, T-test, Information gain, Markov blanket filter |
Abstract |
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Data mining is a form of knowledge discovery essential for solving problems in a specific domain. As the world grows in complexity, overwhelming us with the data it generates, data mining becomes the only hope for elucidating the patterns that underlie it. The manual process of data analysis becomes tedious as size of data grows and the number of dimensions increases, so the process of data analysis needs to be computerized. Feature selection plays an important role in the data mining process. It is very essential to deal with the excessive number of features, which can become a computational burden on the learning algorithms as well as various feature extraction techniques. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks. This paper made a review on various existing feature selection techniques. |
Other Details |
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Paper ID: IJSRDV4I20244 Published in: Volume : 4, Issue : 2 Publication Date: 01/05/2016 Page(s): 325-327 |
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