Feature selection in mammogram image using rough set approach

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. In this work, Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone, requiring no additional information. Analyses more frequently used RST-based traditional feature selection algorithms Quick Reduct Algorithm, Entropy based Reduct Algorithm, Relative Reduct Algorithm. The texture description method GLCM is used to extract Haralick features from mammogram images in different directions. A comparative study is performed and classification has been carried out.