A fuzzy-based methodology for accurate classification and prediction in large datasets.

Data mining and machine learning methods have been utilized successfully in the past for identifying and forecasting meaningful patterns from data repositories of diverse application domains. However, the high number of dimensions and instances present in large datasets pose great technical challenges to these existing methods of classification and prediction. The presence of noisy data and missing values makes it even tougher to achieve accurate prediction outcomes. A number of hybrid methodologies constituting dimensionality reduction, feature selection and noise removal methods have been proposed in the literature. However, majority of these techniques force the analysts to compromise on accuracy of classification and prediction results. Therefore, there is a strong need of a methodology that not only scales well with the sheer size and volume of data but also provides near to accurate classification and prediction results by effectively handling the noise in data variables. This paper proposes a fuzzy-based methodology which ranks the dimensions in order of importance and exploits Fuzzy Nearest Neighbor (FNN) approaches for accurate classification and prediction. An experimental evaluation on real world datasets, taken from UCI machine learning repository, shows that the proposed approach outperforms the existing classification and prediction methods by employing only a subset of important features to achieve high prediction accuracy rates at multiple levels of data abstraction. [ABSTRACT FROM AUTHOR]