A Study on Selection Stability Measures for Various Feature Selection Algorithms

Data mining is indispensable for business organizations for extracting useful information from the stored data which can be used in decision making. Due to the advancements in information technology, these data collected from e-commerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset.