75,151 research outputs found
Stacking classifiers for anti-spam filtering of e-mail
We evaluate empirically a scheme for combining classifiers, known as stacked
generalization, in the context of anti-spam filtering, a novel cost-sensitive
application of text categorization. Unsolicited commercial e-mail, or "spam",
floods mailboxes, causing frustration, wasting bandwidth, and exposing minors
to unsuitable content. Using a public corpus, we show that stacking can improve
the efficiency of automatically induced anti-spam filters, and that such
filters can be used in real-life applications
Using online linear classifiers to filter spam Emails
The performance of two online linear classifiers - the Perceptron and Littlestoneās Winnow ā is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard NaĆÆve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering
Mistake-Driven Learning in Text Categorization
Learning problems in the text processing domain often map the text to a space
whose dimensions are the measured features of the text, e.g., its words. Three
characteristic properties of this domain are (a) very high dimensionality, (b)
both the learned concepts and the instances reside very sparsely in the feature
space, and (c) a high variation in the number of active features in an
instance. In this work we study three mistake-driven learning algorithms for a
typical task of this nature -- text categorization. We argue that these
algorithms -- which categorize documents by learning a linear separator in the
feature space -- have a few properties that make them ideal for this domain. We
then show that a quantum leap in performance is achieved when we further modify
the algorithms to better address some of the specific characteristics of the
domain. In particular, we demonstrate (1) how variation in document length can
be tolerated by either normalizing feature weights or by using negative
weights, (2) the positive effect of applying a threshold range in training, (3)
alternatives in considering feature frequency, and (4) the benefits of
discarding features while training. Overall, we present an algorithm, a
variation of Littlestone's Winnow, which performs significantly better than any
other algorithm tested on this task using a similar feature set.Comment: 9 pages, uses aclap.st
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