A Novel Ensemble Classifier based Classification on Large Datasets with Hybrid Feature Selection Approach

Exploring and analyzing large datasets has become an active research area in the field of data mining in the last two decades. There had been several approaches available in the literature to investigate the large datasets that comprise of millions of data. The most important data mining approaches involved in this task are preprocessing, feature selection and classification. All the three approaches have their own importance in carrying out the task effectively. Most of the existing techniques suffer from drawbacks of high complexity and computationally costly on large data sets. Especially, the classification techniques do not provide consistent and reliable results for large datasets which makes the existing classification systems inefficient and unreliable. This study mainly focuses on develop a novel and efficient framework for analyzing and classifying a large dataset. This study proposes a novel classification approach on large datasets through the process of ensemble classification. Initially, efficient preprocessing approach based on enhanced KNN and feature selection based on genetic algorithm integrated with Kernal PCA are carried out which selects a subset of informative attributes or variables to construct models relating data. Then, Classification is carried on the selected features based on the ensemble approach to get accurate results. This research study presents two types of ensemble classifiers called homogenous and heterogeneous ensemble classifiers to evaluate the performance of the proposed system. Experimental results shows that the proposed approach provide significant results for various large datasets.