Authors: Abdurrahman A. Nasr
Addresses: System and Computer Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
Abstract: Document classification is the task of analysing, identifying and categorising collection of documents into their annotated classes based on their contents. This paper presents ABCD as an agent-based classifier for documents. ABCD is autonomous by depending on software agents in collecting and distributing documents, and smart by exploiting machine learning techniques to train the underlying classifier. As such, the system consists of two essential components, namely, the agent component and the classification component. To be comprehensive and to facilitate comparative results, five statistical classifiers are exploited. These classifiers are based on Naïve Bayes (NB), Hidden Markov Model (HMM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Support Vector Machine (SVM) and Random Forest (RF) algorithms. The proposed model is experimentally tested on both BBC news articles dataset and News Aggregator dataset from artificial intelligence lab. The obtained results indicate the superiority of the Random Forest algorithm for classifying unimodal documents.
Keywords: software agent; supervised learning; random forest; document classification; unimodal document; multi-agent system; data mining.
International Journal of Data Mining, Modelling and Management, 2018 Vol.10 No.3, pp.250 - 268
Received: 14 Feb 2017
Accepted: 27 Sep 2017
Published online: 30 Jul 2018 *