Feature selection for HIV database using rough system

Feature selection studies reveal how to select a subset or list of attributes or variables that are used to construct models describing data. A feature-selection algorithm is part of the classification rule. This is why feature selection must be included when using cross-validation error estimation. Rough Sets theory provides a new mathematical tool to deal with uncertainty and vagueness of an information system in Data mining. The information system may contain a certain amount of redundancy that will not aid knowledge discovery and may in fact mislead the process. The redundant attributes may be eliminated in order to generate the reduct set or to construct the core of the attribute set. This paper proposes Improved Quickreduct Algorithm to select the features from the information system. The experiments are carried out on real HIV patient medical data sets.