The book "Rough Set and Statistical Approach to Knowledge Discovery" provides a comprehensive analysis of the application of Rough Set Theory (RST) in data analysis. In many fields, large amounts of data are collected daily, containing a variety of features that are not all equally important for decision-making processes. RST is increasingly used for dimensionality reduction and feature selection in large datasets. This work investigates how to select the best features from a multitude to develop classifiers and highlights the challenges and solutions associated with the selection of reducts. It also discusses how combining multiple classifiers can lead to improved classification accuracy while considering the complexity of the system. Authors Asit Kumar Das and Jaya Sil offer valuable insights into the balance between efficiency and accuracy in classification.