Machine Learning
Feature selection in SVM text categorization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning from little: comparison of classifiers given little training
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Natural Language Processing and the Web
IEEE Intelligent Systems
Graph-based analysis of semantic drift in Espresso-like bootstrapping algorithms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Optimal feature selection for support vector machines
Pattern Recognition
Proceedings of the 10th annual joint conference on Digital libraries
Selecting few genes for microarray gene expression classification
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
SVM based feature selection: why are we using the dual?
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Entity set expansion using topic information
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Descriminant Words for Problems in Scientific Articles
ICIS '12 Proceedings of the 2012 IEEE/ACIS 11th International Conference on Computer and Information Science
Feature Extraction Using Restricted Bootstrapping
ICIS '12 Proceedings of the 2012 IEEE/ACIS 11th International Conference on Computer and Information Science
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Literature review requires understanding the contents from several view points, such as the problem and the method that the articles describe. Search from these viewpoints will improve the efficiency of survey, if particular segments of articles were extracted, indexed and can be used as auxiliary query. This paper focuses on sentences that describe the problem in an abstract and the feature sets that classify such problem sentences. Classification performance are evaluated by 10-fold cross-validation for six candidate sets of feature words. It turned out that the set of all words gains the best performance if 90% of the data are used as training data. However, the set of a small number of words with positive scores outperforms other feature sets, if the training data is only 10%. In such a realistic situation, the feature words are effective in improving classification performance.