Pattern Recognition Letters - Special issue on genetic algorithms
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mahalanobis-Taguchi System
Dimensionality Reduction in Automatic Knowledge Acquisition: A Simple Greedy Search Approach
IEEE Transactions on Knowledge and Data Engineering
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
A Constrained Genetic Algorithm for Efficient Dimensionality Reduction for Pattern Classification
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Dimensionality Reduction of Clustered Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient ant colony optimization approach to attribute reduction in rough set theory
Pattern Recognition Letters
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
Genetic algorithm-based feature selection in high-resolution NMR spectra
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Two novel feature selection approaches for web page classification
Expert Systems with Applications: An International Journal
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary discriminant analysis
IEEE Transactions on Evolutionary Computation
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Applying the Mahalanobis-Taguchi strategy for software defect diagnosis
Automated Software Engineering
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Binary ant colony optimization applied to variable screening in the Mahalanobis-Taguchi System
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
CAPSO: Centripetal accelerated particle swarm optimization
Information Sciences: an International Journal
| Hi-index | 12.06 |
Mahalanobis-Taguchi System (MTS) is a pattern recognition method applied to classify data into categories - ''healthy'' and ''unhealthy'' or ''acceptable'' and ''unacceptable''. MTS has found applications in a wide range of problem domains. Dimensionality reduction of the input set of attributes forms an important step in MTS. The current practice is to apply Taguchi's design of experiments (DOE) and orthogonal array (OA) method to achieve this end. Maximization of Signal-to-Noise (S/N) ratio forms the basis for selection of the optimal combination of variables. However the DOE-OA method has been reviewed to be inadequate for the purpose. In this research study, we propose a dimensionality reduction method by addressing the problem as feature selection exercise. The optimal combination of attributes minimizes a weighted sum of total fractional misclassification and the percentage of the total number of variables employed to obtain the misclassification. Mahalanobis distances (MDs) of ''healthy'' and ''unhealthy'' conditions are used to compute the misclassification. A mathematical model formulates the feature selection approach and it is solved by binary particle swarm optimization (PSO). Data from an Indian foundry shop is adopted to test the mathematical model and the swarm heuristic. Results are compared with that of DOE-OA method of MTS.