Abstract
Increasing growth of task-oriented texts specifically in organizations, have become a catastrophe nowadays. To overcome this problem, potential classification methods are improved. This paper outlines the capability of neuro-fuzzy approach and artificial immune recognition systems to enhance task-oriented texts classification. Task-oriented texts stand for various kinds of texts which are organized to help the users with their different tasks such as: research, development, learning, justification, innovation and analysis. In this respect, seven major attributes with three nominal values of low, medium and high are considered to classify text into six task classes. To illustrate the capabilities of proposed approaches, Takagi-Sugeno as a neuro-fuzzy approach using lolimot learning algorithm, is compared with multilayer perceptron (MLP), and Radial Basis Function (RBF). In the meantime, various versions of Artificial Immune Recognition Systems (AIRS) including AIRS1, AIRS2, Parallel AIRS and Modified AIRS with Fuzzy K-Nearest neighbor (Fuzzy-KNN) are also evaluated in comparison with the above mentioned algorithms to classify the same text. The experimental results of classification on a dataset of 540 data reveals that, due to the distributed characteristics of a text, and the complexity of tasks respectively, evolutionary and neuro-fuzzy methods are expected to be particularly workable and successfully applicable to task-oriented text classification specifically for the purpose of decision support.