Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Dimensionality Reduction in Automatic Knowledge Acquisition: A Simple Greedy Search Approach
IEEE Transactions on Knowledge and Data Engineering
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Expert Systems with Applications: An International Journal
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Credit risk evaluation using neural networks: Emotional versus conventional models
Applied Soft Computing
Improving the ranking quality of medical image retrieval using a genetic feature selection method
Decision Support Systems
Efficient classifiers for multi-class classification problems
Decision Support Systems
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
Evolutionary joint selection to improve human action recognition with RGB-D devices
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, the feature selection problem was formulated as a multi-objective optimization problem, and new criteria were proposed to fulfill the goal. Foremost, data were pre-processed with missing value replacement scheme, re-sampling procedure, data type transformation procedure, and min-max normalization procedure. After that a wide variety of classifiers and feature selection methods were conducted and evaluated. Finally, the paper presented comprehensive experiments to show the relative performance of the classification tasks. The experimental results revealed the success of proposed methods in credit approval data. In addition, the numeric results also provide guides in selection of feature selection methods and classifiers in the knowledge discovery process.