Review on Machine Learning Feature Selection

Abubakar Atiku Muslim, Muhammad Saidu Aliero

Abstract


Research in machine learning application provides significant improvements in human activities and surroundings. Feature selection is one of the important aspects of model development in machine learning in order to lower the computational cost, increases reliability, and makes the data easier for professionals and machine learning models to understand. There are a number of selection alternatives available, though. In this study, a number of datasets, various tactics from various viewpoints, and a three of the most prominent techniques from various feature selection categories are compared based on analytical evaluation. The findings indicate that, wrapper techniques are more dependable but theoretically costly, filter procedures are usually quicker based on various computational strategies aim to get throughput prediction. 


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References


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