Introduction to algorithms
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Incremental evaluation of computational circuits
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Tractable learning of large Bayes net structures from sparse data
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A dynamic topological sort algorithm for directed acyclic graphs
Journal of Experimental Algorithmics (JEA)
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A batch algorithm for maintaining a topological order
ACSC '10 Proceedings of the Thirty-Third Australasian Conferenc on Computer Science - Volume 102
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Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery approach for support vector machines and propose to discover Markov blankets by performing a fast heuristic Bayesian network structure learning. We give a sufficient condition that our approach will improve the performance. Two major factors that make it prohibitive for learning Bayesian networks from high-dimensional data sets are the large search space and the expensive cycle detection operations. We propose to restrict the search space by only considering the promising candidates and detect cycles using an online topological sorting method. Experimental results show that we can efficiently reduce the feature dimensionality while preserving a high degree of classification accuracy.