User action based adaptive learning with weighted bayesian classification for filtering spam mail

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4304 LNAI, Issue , 2006, Pages 790-798

  Kim, Hyun Jun ^a,b   Shrestha, Jenu ^b   Kim, Heung Nam ^b   Jo, Geun Sik ^b  

^a SAMSUNG Electronics   (South Korea)

^b Inha University   (South Korea)

Author keywords

Adaptive Learning; Concept Drift; Spam Filtering; Weighted Bayesian

Indexed keywords

ADAPTIVE SYSTEMS; ARTIFICIAL INTELLIGENCE; COMPUTER AIDED INSTRUCTION; ELECTRONIC MAIL FILTERS;

ADAPTIVE LEARNING; ADAPTIVE LEARNING ENVIRONMENT; ADAPTIVE LEARNING SYSTEMS; BAYESIAN; BAYESIAN CLASSIFICATION; CONCEPT DRIFTS; PERFORMANCE ENHANCEMENTS; SPAM FILTERING;

LEARNING SYSTEMS;

EID: 52249099141     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11941439_83     Document Type: Conference Paper

References (22)

1. Korea Telecom.: www.kt.co.kr (2004)
2. Sahami, M., Dumais, S., Heckerman, D. and Horvitz, E.: A Bayesian Approach to Filtering Junk E-Mail. In Learning for Text Categorization, Proc. Of the AAAI Workshop, Madison Wisconsin. AAAI Technical Report WS-98-05, (1998) 55-62.
3. Thomas, G. and Peter, A. F.: Weighted Bayesian Classification based on Support Vector Machine, Proc. of the 18th International Conference on Machine Learning, (2001) 207-209.
4. Sakkis G., Androutsopoulos I., Paliouras G., Karkaletsis V., Spyropoulos C.D., Stamatopoulos P.: A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists. Information Retrieval, 6 (1), Kluwer Academic Publishers (2000) 49-73.
5. Androutsopoulos, I., Koutsias, J., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C., Stamatopoulos, P.: Learning to Filter Spam E-mail: A Comparison of a Naïve Bayesian and a Memory-Based Approach. 4th PKDD Workshop on Machine Learning and Textual Information Access, (2000).
6. The Apache SpamAssassin Project, http: //Spamassassin.apache.org/
7. The SpamBayes Project, http: //Spambayes.sourceforge.net/
8. H. J. Kim, H. N. Kim, J. J. Jung, G. S. Jo, : Spam mail Filtering System using Semantic Enrichment, Proc. of the 5th International Conference on Web Information Systems Engineering, (2004).
9. Cunningham P., Nowlan N., Delany S.J., Haahr M.: A Case-Based Approach to Spam Filtering that Can Track Concept Drift, The ICCBR'03 Workshop on Long-Lived CBR Systems, Trondheim, Norway, (2003).
10. Kevin R. G.: Using Latent Semantic Indexing to Filter Spam, ACM Symposium on Applied Computing, Data Mining Track, (2003).
11. Cohen, W. W.: Learning Rules that Classify E-Mail, Proc. Of the AAAI Spring Symposium on Machine Learning in Information Access, (1996).
12. Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D., : An Evaluation of Naive Bayesian Anti-Spam Filtering, Proc. of the ECML 2000 Workshop on Machine Learning in the New Information, (2000) 9-17.
13. Ferreira, J. T. A. S., Denison, D. G. T., Hand, D. J.: Weighted Naïve Bayes modeling for data mining, Technical report, Dept. of mathematics at Imperial College, (2001).
14. H. J. Kim, H. N. Kim, J. J. Jung, G. S. Jo, : On Enhancing The Performance of Spam mail Filtering System using Semantic Enrichment, Proc. of the 17th Australian Joint Conference on Artificial Intelligence (AI-04), (2004).
15. Koychev, I., Schwab I.: Adaption to Drifting User's Interests, Proc. Of the ECML200/MLnet Workshop "ML in the New Information Age", (2000).
16. Pádraig C., Niamh N., Sarah J. D., Mads H.: A Case-Based Approach to Spam Filtering that Can Track Concept Drift, Proc. of the ICCBR03 Workshop on Long-Lived CBR System, (2003).
17. Delany S. J., Cunningham P., Coyle L.: An Assessment of Case-Based Reasoning for Spam Filtering, Artificial Intelligence Review Journal, 24(3-4), Springer, (2005) 359-378.
18. Mitchell T., Caruana R., Freitag D., McDermott J. and Zabowski D.: Experience with a Learning Personal Assistant, Communications of the ACM 37.7, (1994) 81-91.
19. Schlimmer J., Granger R.: Incremental Learning from Noisy Data, Machine Learning1 (3), (1986) 317-357.
20. Grabtree I., Soltysiak S.: Identifying and Tracking Changing Interests, International Journal of Digital Libraries, Springer Verlag, vol. 2, (1998) 38-53.
21. Koychev, I.: Gradual Forgetting for Adaptation to Concept Drift, Proc. Of ECAI 2000 Workshop "Current Issues in Spatio-Temporal Reasoning", (2000) 101-106.
22. Y. Yang, and X. Liu.: A Re-examination of Text Categorization Methods, Proc. of the ACM SIGIR'99 Conference, (1999).