Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization
ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
Unsupervised Elimination of Redundant Features Using Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Collaborative content and user-based web ontology learning system
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Knowledge discovery from text learning for ontology modeling
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Three new feature weighting methods for text categorization
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Feature sub-set selection metrics for Arabic text classification
Pattern Recognition Letters
SOCIFS feature selection framework for handwritten authorship
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
This paper presents a new framework for local featureselection in text filtering. In this framework, a feature setis constructed per category by first selecting a set of termshighly indicative of membership (positive set) and anotherset of terms highly indicative of non-membership (negativeset), and then combining these two sets. This feature selectionframework not only unifies several standard featureselection methods, but also facilitates the proposal of a newmethod that optimally combines the positive and negativesets. The experimental comparison between the proposedmethod and standard methods was conducted on six featureselection metrics: chi-square, correlation coefficient, oddsratio, GSS coefficient and two proposed variants of odds ratioand GSS coefficient: OR-square and GSS-square respectively.The results show that the proposed feature selectionmethod improves text filtering performance.