An incremental spam detection algorithm

Ghanbari, Elham; Beigy, Hamid

doi:10.1109/aisp.2011.5960991

Cited by 2 publications

(1 citation statement)

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“…The training instances for each classifier are obtained from an iteratively updated distribution. The main difference between these two algorithms is on the distribution update rule [25]. In RotBoost, the updating rule is based on the performance of the previous classifier [22], whereas, the updating rule of RotBoost algorithm is based on the performance of the entire ensemble, which means the focus of this algorithm is on the samples containing new knowledge.…”

Section: The Proposed Incremental Learning Algorithmmentioning

confidence: 99%

Incremental RotBoost algorithm: An application for spam filtering

Ghanbari

Beigy

2015

IDA

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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.

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Section: The Proposed Incremental Learning Algorithmmentioning

confidence: 99%