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

"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance

Multi-classifier classification of spam email on an ubiquitous multi-core architecture

This paper presents an innovative fusion based multi-classifier email classification on a ubiquitous multi-core architecture. Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus. We build upon our previous work on multi-core by apply our ubiquitous multi-core framework to run our fusion based multi-classifier architecture. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our proposed multi-classifier based filtering system. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at the average cost of 1.4 ms. We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification techniques.<br /

Hybrid GA-SVM for Efficient Feature Selection in E-mail Classification

Feature selection is a problem of global combinatorial optimization in machine learning in which subsets of relevant features are selected to realize robust learning models. The inclusion of irrelevant and redundant features in the dataset can result in poor predictions and high computational overhead. Thus, selecting relevant feature subsets can help reduce the computational cost of feature measurement, speed up learning process and improve model interpretability. SVM classifier has proven inefficient in its inability to produce accurate classification results in the face of large e-mail dataset while it also consumes a lot of computational resources. In this study, a Genetic Algorithm-Support Vector Machine (GA-SVM) feature selection technique is developed to optimize the SVM classification parameters, the prediction accuracy and computation time. Spam assassin dataset was used to validate the performance of the proposed system. The hybrid GA-SVM showed remarkable improvements over SVM in terms of classification accuracy and computation time. Keywords: E-mail Classification, Feature-Selection, Genetic algorithm, Support Vector Machin

Identification of Informativeness in Text using Natural Language Stylometry

In this age of information overload, one experiences a rapidly growing over-abundance of written text. To assist with handling this bounty, this plethora of texts is now widely used to develop and optimize statistical natural language processing (NLP) systems. Surprisingly, the use of more fragments of text to train these statistical NLP systems may not necessarily lead to improved performance. We hypothesize that those fragments that help the most with training are those that contain the desired information. Therefore, determining informativeness in text has become a central issue in our view of NLP. Recent developments in this field have spawned a number of solutions to identify informativeness in text. Nevertheless, a shortfall of most of these solutions is their dependency on the genre and domain of the text. In addition, most of them are not efficient regardless of the natural language processing problem areas. Therefore, we attempt to provide a more general solution to this NLP problem. This thesis takes a different approach to this problem by considering the underlying theme of a linguistic theory known as the Code Quantity Principle. This theory suggests that humans codify information in text so that readers can retrieve this information more efficiently. During the codification process, humans usually change elements of their writing ranging from characters to sentences. Examples of such elements are the use of simple words, complex words, function words, content words, syllables, and so on. This theory suggests that these elements have reasonable discriminating strength and can play a key role in distinguishing informativeness in natural language text. In another vein, Stylometry is a modern method to analyze literary style and deals largely with the aforementioned elements of writing. With this as background, we model text using a set of stylometric attributes to characterize variations in writing style present in it. We explore their effectiveness to determine informativeness in text. To the best of our knowledge, this is the first use of stylometric attributes to determine informativeness in statistical NLP. In doing so, we use texts of different genres, viz., scientific papers, technical reports, emails and newspaper articles, that are selected from assorted domains like agriculture, physics, and biomedical science. The variety of NLP systems that have benefitted from incorporating these stylometric attributes somewhere in their computational realm dealing with this set of multifarious texts suggests that these attributes can be regarded as an effective solution to identify informativeness in text. In addition to the variety of text genres and domains, the potential of stylometric attributes is also explored in some NLP application areas---including biomedical relation mining, automatic keyphrase indexing, spam classification, and text summarization---where performance improvement is both important and challenging. The success of the attributes in all these areas further highlights their usefulness

Spam Filter Improvement Through Measurement

This work supports the thesis that sound quantitative evaluation for spam filters leads to substantial improvement in the classification of email. To this end, new laboratory testing methods and datasets are introduced, and evidence is presented that their adoption at Text REtrieval Conference (TREC)and elsewhere has led to an improvement in state of the art spam filtering. While many of these improvements have been discovered by others, the best-performing method known at this time -- spam filter fusion -- was demonstrated by the author. This work describes four principal dimensions of spam filter evaluation methodology and spam filter improvement. An initial study investigates the application of twelve open-source filter configurations in a laboratory environment, using a stream of 50,000 messages captured from a single recipient over eight months. The study measures the impact of user feedback and on-line learning on filter performance using methodology and measures which were released to the research community as the TREC Spam Filter Evaluation Toolkit. The toolkit was used as the basis of the TREC Spam Track, which the author co-founded with Cormack. The Spam Track, in addition to evaluating a new application (email spam), addressed the issue of testing systems on both private and public data. While streams of private messages are most realistic, they are not easy to come by and cannot be shared with the research community as archival benchmarks. Using the toolkit, participant filters were evaluated on both, and the differences found not to substantially confound evaluation; as a result, public corpora were validated as research tools. Over the course of TREC and similar evaluation efforts, a dozen or more archival benchmarks -- some private and some public -- have become available. The toolkit and methodology have spawned improvements in the state of the art every year since its deployment in 2005. In 2005, 2006, and 2007, the spam track yielded new best-performing systems based on sequential compression models, orthogonal sparse bigram features, logistic regression and support vector machines. Using the TREC participant filters, we develop and demonstrate methods for on-line filter fusion that outperform all other reported on-line personal spam filters