Automated text classification using a dynamic artificial neural network model

Widespread digitization of information in today’s internet age has intensified the need for effective textual document classification algorithms. Most real life classification problems, including text classification, genetic classification, medical classification, and others, are complex in nature and are characterized by high dimensionality. Current solution strategies include Naïve Bayes (NB), Neural Network (NN), Linear Least Squares Fit (LLSF), k-Nearest-Neighbor (kNN), and Support Vector Machines (SVM); with SVMs showing better results in most cases. In this paper we introduce a new approach called dynamic architecture for artificial neural networks (DAN2) as an alternative for solving textual document classification problems. DAN2 is a scalable algorithm that does not require parameter settings or network architecture configuration. To show DAN2 as an effective and scalable alternative for text classification, we present comparative results for the Reuters-21578 benchmark dataset. Our results show DAN2 to perform very well against the current leading solutions (kNN and SVM) using established classification metrics.

► We introduce the dynamic artificial neural network (DAN2) for text classification.
► DAN2 is a scalable algorithm, not requiring user defined configuration.
► DAN2 is compared with k Nearest Neighbor (kNN) and Support Vector Machines (SVM).
► DAN2 is shown to outperform both kNN and SVM with this data set.