Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Feature selection on hierarchy of web documents
Decision Support Systems - Web retrieval and mining
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
Text Retrieval from Document Images Based on Word Shape Analysis
Applied Intelligence
On Machine Learning Methods for Chinese Document Categorization
Applied Intelligence
Authorship Attribution with Support Vector Machines
Applied Intelligence
Implementation of the SMART Information Retrieval System
Implementation of the SMART Information Retrieval System
An introduction to variable and feature selection
The Journal of Machine Learning Research
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
A Hierarchical Neural Network Document Classifier with Linguistic Feature Selection
Applied Intelligence
Raising the baseline for high-precision text classifiers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Asymmetric support vector machines: low false-positive learning under the user tolerance
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Imbalanced text classification: A term weighting approach
Expert Systems with Applications: An International Journal
Feature selection for text classification with Naïve Bayes
Expert Systems with Applications: An International Journal
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fraudulent and malicious sites on the web
Applied Intelligence
On strategies for imbalanced text classification using SVM: A comparative study
Decision Support Systems
Analytical evaluation of term weighting schemes for text categorization
Pattern Recognition Letters
Imbalanced classification using support vector machine ensemble
Neural Computing and Applications
A real-time transportation prediction system
Applied Intelligence
| Hi-index | 0.00 |
In this study, the differences among widely used weighting schemes are studied by means of ordering terms according to their discriminative abilities using a recently developed framework which expresses term weights in terms of the ratio and absolute difference of term occurrence probabilities. Having observed that the ordering of terms is dependent on the weighting scheme under concern, it is emphasized that this can be explained by the way different schemes use term occurrence differences in generating term weights. Then, it is proposed that the relevance frequency which is shown to provide the best scores on several datasets can be improved by taking into account the way absolute difference values are used in other widely used schemes. Experimental results on two different datasets have shown that improved F 1 scores can be achieved.