A Genetic-Fuzzy Approach for Automatic Text Categorization

The rapid growth of World Wide Web has resulted in massive information from varied sources rising at an exponential rate. The high availability of such disparate information has precipitated the need of automatic text categorization for managing, organizing huge data and knowledge discovery. Main challenges of text classification include high dimensionality of feature space and classification accuracy. Thus, to make classifiers more accurate and efficient, there arises the need of Feature Selection. Genetic algorithms have gained much attention over traditional methods due to its simplicity and robustness to solve the optimization problem and high exponential search ability. Thus, the paper focuses on using Genetic Algorithm (GA) for Feature Selection to obtain optimal features for classifying unstructured data. We build a fuzzy rule-based classifier that automatically generates fuzzy rules for classification. The experiments are conducted on two-datasets namely 20-Newsgroup and Reuters-21578 and the results indicate that GA outperforms Principal Component Analysis (PCA).