A fuzzy-based instance selection approach for data mining

Data mining is an area that is enjoying increasing growth. One of the most time-consuming tasks in data mining is data preparation or pre-processing. Since data pre-processing takes more time and effort than the rest of the data mining process, the need for improved data pre-processing methods is well recognized. Dealing with missing values can further complicate data pre-processing. Several methods have been used to resolve the problem of instance selection when there are missing data values. Most of these methods: 1) discard records with missing values; 2) use all records and ignore missing values; or 3) use all records and infer missing values. These methods do not consider the utility of individual attributes. Here, we introduce a fuzzy-based information metric that considers the usefulness of the individual attributes by incorporating domain knowledge into a multicriteria decision-making instance selection technique