C4.5: programs for machine learning
C4.5: programs for machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Machine Learning
Machine Learning
Machine Learning
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
An introduction to variable and feature selection
The Journal of Machine Learning Research
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
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This paper proposes a novel feature ranking method, DensityRank, based on kernel estimation on the feature spaces to improve the classification performance. As the availability of raw data in many of today's applications continues to grow at an explosive rate, it is critical to assess the learning capabilities of different features and select the important subset of features to improve learning accuracy as well as reduce computational cost. In our approach, kernel methods are used to estimate the probability density function for each feature across different class labels. Discrepancy analysis based on the mean integrated square error (MISE) between pairs of such density estimations is used to provide the ranking values. Then, the ranked subspace method is adopted to select subsets of important features that are used to develop the learning models. Comparative study of this method with those of traditional ranking methods related to Fisher's discrimination ratio and information gain theory, as well as the random subspace algorithm and the bootstrap aggregating (bagging), are presented in this paper. Simulation results on various real-world data sets illustrate the effectiveness of the proposed method.