• Rolly Gupta
• Dr. Sangeeta Sabharwal
• Dr. Anjana Gosain

A data warehouse consists of multiple views that can be accessed by the means of queries. One of the vital decisions in designing a data warehouse is the selection of materialized views for the purpose of an efficient decision making. The search space for the selection method of materialized views is exponentially very large enough; therefore, heuristics have been used for searching a small fraction of the space to get into a near optimal solution. Feature selection is of immense importance in the field of statistical data analysis. The dimensionality of data makes the testing and training process of general classification methods more difficult. Materialization on the reduced set of attributes reduces the computation time and also helps to make the patterns easier to understand. In this paper, we will consider some classification and regression approach to explain the importance of model-based feature selection. Finally, all the different classifiers used for feature selection are also compared and accordingly best classifier for respective datasets is observed.

Dimension reduction, Classification, Regression

"Model Based Feature Selection For View Maintenance Models Of Data Warehouse", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.609-615, May-2019, Available :http://www.jetir.org/papers/JETIR1905290.pdf

"Model Based Feature Selection For View Maintenance Models Of Data Warehouse", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp609-615, May-2019, Available at : http://www.jetir.org/papers/JETIR1905290.pdf