Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
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)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Proceedings of the 25th international conference on Machine learning
BNS feature scaling: an improved representation over tf-idf for svm text classification
Proceedings of the 17th ACM conference on Information and knowledge management
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel traffic analysis for identifying search fields in the long tail of web sites
Proceedings of the 19th international conference on World wide web
Contributions to the study of SMS spam filtering: new collection and results
Proceedings of the 11th ACM symposium on Document engineering
Intelligible models for classification and regression
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
Linear classifiers have been shown to be effective for many discrimination tasks. Irrespective of the learning algorithm itself, the final classifier has a weight to multiply by each feature. This suggests that ideally each input feature should be linearly correlated with the target variable (or anti-correlated), whereas raw features may be highly non-linear. In this paper, we attempt to re-shape each input feature so that it is appropriate to use with a linear weight and to scale the different features in proportion to their predictive value. We demonstrate that this pre-processing is beneficial for linear SVM classifiers on a large benchmark of text classification tasks as well as UCI datasets.