A Survey on Feature SubSet Selection Algorithm Using Filter Method

Data Mining is at present an extended field of research that digs out new patterns used in decision making applications. It is used to crack problems of an explicit realm. Due to amplified data density, the magnitude of data to be mined also has increased. It involves a pre- processing step called Feature Subset Selection to act upon the Knowledge Discovery Process. The insignificant attributes irrelevant to the class of study must be removed from the existing databases. This filtering process is called Feature Subset Selection. This study involves the various methods used for the dimensionality reduction process. This survey takes into account the diverse kinds of Feature Subset Selection methods used to discover the best features and remove statistically insignificant values. This survey also provides a subsidiary idea for future enhancement in this field.