Download e-book for kindle: Advances in Artificial Intelligence: 23rd Canadian by Atefeh Farzindar, Vlado Keselj

By Atefeh Farzindar, Vlado Keselj

ISBN-10: 3642130585

ISBN-13: 9783642130588

This publication constitutes the refereed complaints 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 offered including 26 revised brief papers, 12 papers from the graduate scholar symposium and the abstracts of three keynote shows 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 computer studying; neural networks and swarm optimization; laptop studying and information mining; ordinary language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; desktop studying; and information mining.

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Additional info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010,

Example text

Naive Bayesian results with λ = 1 Classified legitimate Classified spam Total 5 Actually legitimate Actually spam 476 32 23 236 499 268 Total 508 259 767 Experimental Results and Evaluations Our experiments were performed on a spambase data set from UCI Machine Learning Repository [10]. The data set consists of 4601 instances, with 1813 instances as spam, and 2788 instances as legitimate, each instance is described by 58 attributes. Our goal is to compare our approach with the original naive Bayesian spam filter in terms of the error rate that a legitimate email is classified as spam, the precision and recall for both legitimate and spam emails, and the cost-sensitive measure suggested by Androutsopoulos et al.

PN − λNN ) + (λNP − λPP ) α= (15) The decision rules (P)-(N) can be expressed concisely as: (P) If P r(C|x) ≥ α and P r(C|x) ≥ γ, decide x ∈ POS(C); (B) If P r(C|x) ≤ α and P r(C|x) ≥ β, decide x ∈ BND(C); (N) If P r(C|x) ≤ β and P r(C|x) ≤ γ, decide x ∈ NEG(C). Each rule is defined by two out of the three parameters. The conditions of rule (B) suggest that α > β may be a reasonable constraint; it will ensure a well-defined boundary region. By setting α > β, namely, (λPN − λBN ) (λBN − λNN ) > , (λPN − λBN ) + (λBP − λPP ) (λBN − λNN ) + (λNP − λBP ) (16) we obtain the following condition on the loss function [17]: (c1).

1]. We split the spambase data set into a training set of 3834 instances, and a testing set of 767 instances. Since the attributes in the input data set have continuous values, entropy-MDL [4] is used as the discretization method applied to both the training and testing data sets before the calculations of probabilities. For the cost-sensitive evaluations, we assume that misclassifying a legitimate email as spam is λ times more costly than misclassifying a spam email as legitimate. We considered three different λ values (λ = 9, λ = 3, and λ = 1) for the original naive Bayesian spam filter.

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Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, by Atefeh Farzindar, Vlado Keselj


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