Enhanced multi-label arabic text classification based on integration of particle swarm algorithm and machine learning models
Multi-label text categorization is an important modern text mining task. The large number of feature in text datasets degrades the performance of text classification. However, multi-label text often has more noisy, irrelevant and redundant features with high dimensionality. A large amount of computational time is required to classify a large number of text documents of high dimensional. The problem is much difficult in Arabic due to complex nature of the Arabic language, which has a very rich and complicated morphology.