An ensemble method is an approach where several classifiers are created from the training data which can be often more accurate than any of the single classifiers, especially if the base classifiers are accurate and different one each other. Menawhile, feature clustering can reduce feature space by joining similar words into one cluster. The objective of this research is to develop a text categorization system that employs feature clustering based on ensemble feature selection. The research methodology consists of text documents preprocessing, feature subspaces generation using the genetic algorithm-based iterative refinement, implementation of base classifiers by applying feature clustering, and classification result integration of each base classifier using both the static selection and majority voting methods. Experimental results show that the computational time consumed in classifying the dataset into 2 and 3 categories using the feature clustering method is 1.18 and 27.04 seconds faster in compared to those that do not employ the feature selection method, respectively. Also, using static selection method, the ensemble feature selection method with genetic algorithm-based iterative refinement produces 10% and 10.66% better accuracy in compared to those produced by the single classifier in classifying the dataset into 2 and 3 categories, respectively. Whilst, using the majority voting method for the same experiment, the similar ensemble method produces 10% and 12% better accuracy than those produced by the single classifier, respectively.
LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition
