C4.5: programs for machine learning
C4.5: programs for machine learning
Relevance feedback and inference networks
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
An algorithm for suffix stripping
Readings in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Extracting query modifications from nonlinear SVMs
Proceedings of the 11th international conference on World Wide Web
Machine Learning
Modern Information Retrieval
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW
IEEE Transactions on Knowledge and Data Engineering
An evaluation of term dependence models in information retrieval
SIGIR '82 Proceedings of the 5th annual ACM conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The VLDB Journal — The International Journal on Very Large Data Bases
Improving Category Specific Web Search by Learning Query Modifications
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Integrating induction and deduction for noisy data mining
Information Sciences: an International Journal
A schema for ontology-based concept definition and identification
International Journal of Computer Applications in Technology
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Traditional document classification frameworks, which apply the learned classifier to each document in a corpus one by one, are infeasible for extremely large document corpora, like the Web or large corporate intranets. We consider the classification problem on a corpus that has been processed primarily for the purpose of searching, and thus our access to documents is solely through the inverted index of a large scale search engine. Our main goal is to build the “best” short query that characterizes a document class using operators normally available within search engines. We show that surprisingly good classification accuracy can be achieved on average over multiple classes by queries with as few as 10 terms. As part of our study, we enhance some of the feature-selection techniques that are found in the literature by forcing the inclusion of terms that are negatively correlated with the target class and by making use of term correlations; we show that both of those techniques can offer significant advantages. Moreover, we show that optimizing the efficiency of query execution by careful selection of terms can further reduce the query costs. More precisely, we show that on our set-up the best 10-term query can achieve 93% of the accuracy of the best SVM classifier (14,000 terms), and if we are willing to tolerate a reduction to 89% of the best SVM, we can build a 10-term query that can be executed more than twice as fast as the best 10-term query.