Ensemble learning approach in Improved K Nearest Neighbor algorithm for Text Categorization

Due to the tremendous growth of digital content in World Wide Web (WWW), Text categorization has become an important tool to manage and organize text related data. This paper proposes an Ensemble Learning approach in Improved K Nearest Neighbor algorithm for Text Categorization (EINNTC), which consists of single pass clustering, Ensemble learning and KNN algorithm. The EINNTC method provides solution to traditional KNN classifier issues, by reducing the huge text similarity computation complexity, avoids an impact of noisy training sample, and expediting the process of finding K nearest neighbors. The experiments were carried out with standard benchmark Reuters dataset, and their empirical results shows that the proposed method outperforms the SVM and KNN classifiers.