A Survey on Clustered Feature Selection Algorithms for High Dimensional Data

Provided by: International Journal of Computing Science and Information Technology (IJCSIT)
Topic: Big Data
Format: PDF
In machine learning, feature selection is preprocessing step and can be effectively reduce high dimensional data, remove irrelevant data, increase learning accuracy, and improve result comprehensibility. High dimensionality of data take over efficiency and effectiveness points of view in feature selection algorithm. Efficiency stands required time to find a subset of features, and the effectiveness belongs to good quality of the subset of features. In feature selection technique high dimensional data contains many irrelevant and redundant features.

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