An Improved Flower Pollination Algorithm with AdaBoost Algorithm for Feature Selection in Text Documents Classification

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space. Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature space without FS increases the computational cost which is a function of the length of the vector, and also, it helps to remove irrelevant attributes. The general approach in this paper combines the hybrid of Flower Pollination Algorithm (FPA) with Ada-Boost algorithm. The FPA is used for FS and the Ada-Boost is used for classification of text documents. Tests were conducted on Reuters-21578, WEBKB and CADE 12 datasets. The results show that the hybrid model has higher detection accuracy in FS compared with Ada-Boost algorithm with model. And comparisons are indicative of higher detection accuracy of the proposed model compared with KNN-K-Means, NB-K-Means and learning models.