The cascade-correlation learning architecture
Advances in neural information processing systems 2
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
Randomized Variable Elimination
The Journal of Machine Learning Research
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
Hybrid approaches for classification under information acquisition cost constraint
Decision Support Systems
Expert Systems with Applications: An International Journal
A global-ranking local feature selection method for text categorization
Expert Systems with Applications: An International Journal
Using genetic algorithm based knowledge refinement model for dividend policy forecasting
Expert Systems with Applications: An International Journal
Fusion of feature sets and classifiers for facial expression recognition
Expert Systems with Applications: An International Journal
A misclassification cost risk bound based on hybrid particle swarm optimization heuristic
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
We consider a feature selection problem where the decision-making objective is to minimize overall misclassification cost by selecting relevant features from a training dataset. We propose a two-stage solution approach for solving misclassification cost minimizing feature selection (MCMFS) problem. Additionally, we propose a maximum-margin genetic algorithm (MMGA) that maximizes margin of separation between classes by taking into account all examples as opposed to maximizing margin of separation using a few support vectors. Feature selection is carried out by either an exhaustive or a heuristic simulated annealing approach in the first stage and a cost sensitive classification using either MMGA or cost sensitive support vector machines (SVM) in the second stage. Using simulated and real-world data sets and different misclassification cost matrices, we test our two-stage approach for solving the MCMFS problem. Our results indicate that feature selection plays an important role when misclassification cost asymmetries increase and the MMGA shows equal or better performance than the SVM.