A Survey on Feature Selection in Data Mining

Feature Selection is a fundamental problem in machine learning and data mining . Feature Selection is an effective way for reducing dimensionality, removing irrelevant data increasing learning accuracy. Feature Selection is the process of identifying a subset of the most useful features that produce compatible results as the original entire set of features .A Feature Selection techniques may be evaluated from both efficiency and effectiveness point of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of the subset of features. Feature Selection is different from dimensionality reduction. Both methods search for to reduce the number of attributes in the dataset. But dimensionality reduction method creating new combination of attributes. Feature Selection methods include and exclude attributes present in the data without change. The central assumption when using a Feature Selection technique is that the data contains many redundant or irrelevant features. This paper actually a survey on various technique of feature selection and its advantages disadvantages.