Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machines: relevance feedback and information retrieval
Information Processing and Management: an International Journal
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
An Innovative Spam Filtering Model Based on Support Vector Machine
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
An innovative analyser for multi-classifier e-mail classification based on grey list analysis
Journal of Network and Computer Applications
Minimizing the Limitations of GL Analyser of Fusion Based Email Classification
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
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Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.