Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.
The influences of membrane composition and surface morphology for membranes composed of polyethersulfone (PES) and fumarate alumoxane (Fum-A) on fouling were investigated. Membrane fouling and blocking mechanisms were analyzed using the Hermia and combined pore blocking models. According to the Hermia fitted parameters, nanocomposite membranes revealed better antifouling properties. The lowest fouling coefficient belonged to the 2 wt % Fum-A/PES membrane. In whey filtration, membrane surface morphology did not seem to have an important impact on the blocking mechanism as hydrophilicity. However, results clearly indicated a strong correlation between the surface morphology and the contribution of each individual fouling mechanism in the combined pore blocking/cake formation models.
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