Experience with a learning personal assistant
Communications of the ACM
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Incremental Learning from Noisy Data
Machine Learning
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
An Assessment of Case-Based Reasoning for Spam Filtering
Artificial Intelligence Review
On enhancing the performance of spam mail filtering system using semantic enrichment
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Which work-item updates need your response?
Proceedings of the 10th Working Conference on Mining Software Repositories
| Hi-index | 0.00 |
Nowadays, e-mail is considered one of the most important communication methods, but most users suffer from Spam mail. To solve this problem, there has been much research. The previous research showed comparatively high performance, but for adaptation of real world, it requires several improvements. First, it needs personalized learning for better performance. We cannot make a strict definition of Spam, because the definition of any context depends on each user. Second, the concept drift or interest drift problem, that is, users' interest or any context's concept, may change over time. Therefore, many Spam filtering systems are using continuous learning schemes such as adaptive learning or incremental learning. However, these systems require user feedback or rating results manually, and this inconvenience causes slow learning and performance enhancement. In this research, we developed an adaptive learning system based on an automatic weighting environment. For the automatic weight, we categorized 6 user patterns (actions) on the mailing system whose weights are automatically adapted to the learning phase. From the experiment, we will demonstrate the Bayesian classification with an adaptive learning environment. By using suggesting ideas, we will analyze the comparison result with adaptive learning. Finally, from the experiment using real world data sets, we will prove its possibility for tracking the concept and interest drift problems.