Incremental RotBoost algorithm: An application for spam filtering

Article type: Research Article

Authors: Ghanbari, Elham^{; *} | Beigy, Hamid

Affiliations: Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

Correspondence: [*] Corresponding author: Elham Ghanbari, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. E-mail: beigy@sharif.edu

Abstract: Incremental learning is a learning algorithm that can get new information from new training sets without forgetting the acquired knowledge from the previously used training sets. In this paper, an incremental learning algorithm based on ensemble learning is proposed. Then, an application of the proposed algorithm for spam filtering is discussed. The proposed algorithm called incremental RotBoost, assumes the environment is stationary. It trains new weak classifiers for newly arriving data, which are added to the ensemble of classifiers. To evaluate the performance of the proposed algorithm, several computer experiments are conducted. The results of computer experiments show the ability of our proposed algorithm for different tasks in the incremental learning. The results also demonstrate that the proposed algorithm can learn incrementally, and it can learn new classes, as well.

Keywords: Ensemble learning, spam detection, incremental learning

DOI: 10.3233/IDA-150725

Journal: Intelligent Data Analysis, vol. 19, no. 2, pp. 449-468, 2015