By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)
This ebook constitutes the refereed court cases of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers awarded including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote displays have been rigorously reviewed and chosen from ninety submissions. The papers are prepared in topical sections on textual content type; textual content summarization and IR; reasoning and e-commerce; probabilistic desktop studying; neural networks and swarm optimization; laptop studying and knowledge mining; common language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; laptop studying; and information mining.
Read Online or Download Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings PDF
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Extra resources for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings
We also ﬁnd that when λ changed to a bigger value, the performances of both approaches are increased, but the threeway decision approach performs out the naive Baysian spam ﬁlter in all three settings. 6 Conclusion In this paper, we present a three-way decision approach to email spam ﬁltering. In addition to the most commonly used binary classiﬁcation for spam ﬁltering, a third action is added to allow users make further examinations for undecided cases. The main advantage of our approach is that it provides a more sensible feedback to users for handling their emails, thus reduces the misclassiﬁcation rate.
H. Razavi et al. We used a total number of 1525 messages (1038 (68%) Okay and 487 (32%) Flame), from the two datasets together, from which 10% was used as a test set, and the rest was used as training set for our multi-level classifier. 4 Methodology After data preprocessing1, we run a three-level classification for flame detection. Considering the attributes of each level we tried most of the applicable machine learning algorithms implemented in Weka (the standard machine learning software developed at the University of Waikato) .
As we apply some patterns from IALD, as well as classifier models for flame detection, it is important to prevent training the classifiers based on some instances in which the assigned labels are opposed to some of IALD built-in weighted patterns and vice versa. Otherwise, the system will suffer from a considerable level of noise in the data. 7 Conclusion and Future Work We designed and implemented novel and very efficient flame detection software. It applies models from multi-level classifiers, boosted by an Insulting and Abusing Language Dictionary.
Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)