Abstract
Now a days, there is rapid development is Computer Science and Engineering Technology as well as data has been increasing wildly and it is a major problem for users to quickly find the most relevant (or useful) information or data from large amount of data being stored in databases. To solve this problem so many researchers are found that the feature selection algorithms (or methods) are best methods to identify to useful data (or information) from large amount of data present in databases. Feature Selection algorithm is useful in identifying (or selecting) most useful information from the entire large original set of features to improve accuracy. Feature Selection specifies a task that selects a subset of features and those are useful to solve domain problems. There are several important algorithms (or methods) are available for selecting the relevant features from large set of features to improve accuracy of the results. This paper explains performance of various feature selection algorithms to find most relevant features from the entire set of features.
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Bharat, M., Raveendra, K., Ravi Kumar, Y., Santhi Sree, K. (2017). A Selective Data on Performance Feature with Selective Algorithms. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-10-3818-1_41
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DOI: https://doi.org/10.1007/978-981-10-3818-1_41
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