کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410501 | 679147 | 2013 | 13 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: An ontology enhanced parallel SVM for scalable spam filter training An ontology enhanced parallel SVM for scalable spam filter training](/preview/png/410501.png)
Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart.
Journal: Neurocomputing - Volume 108, 2 May 2013, Pages 45–57