Maximum weight and minimum redundancy: A novel framework for feature subset selection

Authors:
Jianzhong Wang;Lishan Wu;Jun Kong;Yuxin Li;Baoxue Zhang
Affiliations:
College of Computer Science and Information Technology, Northeast Normal University, Changchun, China and National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal ...;College of Computer Science and Information Technology, Northeast Normal University, Changchun, China and Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Norm ...;College of Computer Science and Information Technology, Northeast Normal University, Changchun, China and Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Norm ...;National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun, China;Key Laboratory for Applied Statistics of MOE, Changchun, China
Venue:
Pattern Recognition
Year:
2013

Citing 17
Cited 1

From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Solving Standard Quadratic Optimization Problems via Linear, Semidefinite and Copositive Programming

Journal of Global Optimization
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
The CMU Pose, Illumination, and Expression Database

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy

The Journal of Machine Learning Research
Acquiring Linear Subspaces for Face Recognition under Variable Lighting

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Constraint Score: A new filter method for feature selection with pairwise constraints

Pattern Recognition
Feature construction from synergic pairs to improve microarray-based classification

Bioinformatics
Searching for interacting features

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation

Computer Vision and Image Understanding
Dense Neighborhoods on Affinity Graph

International Journal of Computer Vision
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Fusion of Band Limited Phase Only Correlation and Width Centroid Contour Distance for finger based biometrics

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

Feature subset selection is often required as a preliminary work for many pattern recognition problems. In this paper, a novel filter framework is presented to select optimal feature subset based on a maximum weight and minimum redundancy (MWMR) criterion. Since the weight of each feature indicates its importance for some ad hoc tasks (such as clustering and classification) and the redundancy represents the correlations among features. Through the proposed MWMR, we can select the feature subset in which the features are most beneficial to the subsequent tasks while the redundancy among them is minimal. Moreover, a pair-wise updating based iterative algorithm is introduced to solve our framework effectively. In the experiments, three feature weighting algorithms (Laplacian score, Fisher score and Constraint score) are combined with two redundancy measurement methods (Pearson correlation coefficient and Mutual information) to test the performances of proposed MWMR. The experimental results on five different databases (CMU PIE, Extended YaleB, Colon, DLBCL and PCMAC) demonstrate the advantage and efficiency of our MWMR.