An Efficient Mining Model For Enhancing Text Classification Using k-NN

Text classification is a supervised technique that uses labeled training data to learn the classification system and then automatically classifies the remaining text using the learned system. Classification plays a vital role in many information management and retrieval tasks. Classification includes different parts such as text processing, feature extraction, feature vector construction and final classification. In this project, apply machine learning methods for classification. In this regard, first try to exert some text preprocess in different dataset, and then extract a feature vector for each new document by using feature weighting and feature selection algorithms for enhancing the text classification accuracy. After that train our classifier by Naïve Bayesian (NB) and K-nearest neighbor (KNN) algorithms. In experiments, both algorithms show acceptable results for text classification.