Laplacian Score and Top Scoring Pair Feature Selection Algorithms

The method by which data from the gene of a human body is converted into a protein, where it can be used to infer how active a gene is called gene expression. Recently, gene expression is applied for disease identification in human body. As the gene data is high dimensional when compared with the number of samples available, gene expression data can be used for the prognosis of diseases like cancer, which is caused when the cells undergo an uncontrollable division and spread into the surrounding tissues. In the case of a prediction system, we need effective datasets to deliver maximum prediction accuracy. In order to transform data from high dimensional data to the most effective data Feature selection is used. Feature selection selects the most appropriate or required features into consideration. This technique has different methods by which it can be executed, they are open-loop method/filter method, close-loop method/wrapper method, embedded method, and the ensemble method. The proposed research work is based on two feature selection algorithms namely, Laplacian score, an open-loop method, and Top scoring pair, a hybrid method.