Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data preparation for data mining
Data preparation for data mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The Journal of Machine Learning Research
Using discriminant analysis for multi-class classification: an experimental investigation
Knowledge and Information Systems
Analysis of new variable selection methods for discriminant analysis
Computational Statistics & Data Analysis
Non-parametric classifier-independent feature selection
Pattern Recognition
Engineering Applications of Artificial Intelligence
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
A note on two-dimensional linear discriminant analysis
Pattern Recognition Letters
Rough set theory with discriminant analysis in analyzing electricity loads
Expert Systems with Applications: An International Journal
Identification of citrus disease using color texture features and discriminant analysis
Computers and Electronics in Agriculture
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing performances of backpropagation and genetic algorithms in the data classification
Expert Systems with Applications: An International Journal
CROS: A Contingency Response multi-agent system for Oil Spills situations
Applied Soft Computing
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic Computing
Information Sciences: an International Journal
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Linear discriminant analysis (LDA) is a commonly used classification method. It can provide important weight information for constructing a classification model. However, real-world data sets generally have many features, not all of which benefit the classification results. If a feature selection algorithm is not employed, unsatisfactory classification will result, due to the high correlation between features and noise. This study points out that the feature selection has influence on the LDA by showing an example. The methods traditionally used for LDA to determine the beneficial feature subset are not easy or cannot guarantee the best results when problems have larger number of features. The particle swarm optimization (PSO) is a powerful meta-heuristic technique in the artificial intelligence field; therefore, this study proposed a PSO-based approach, called PSOLDA, to specify the beneficial features and to enhance the classification accuracy rate of LDA. To measure the performance of PSOLDA, many public datasets are employed to measure the classification accuracy rate. Comparing the optimal result obtained by the exhaustive enumeration, the PSOLDA approach can obtain the same optimal result. Due to much time required for exhaustive enumeration when problems have larger number of features, exhaustive enumeration cannot be applied. Therefore, many heuristic approaches, such as forward feature selection, backward feature selection, and PCA-based feature selection are used. This study showed that the classification accuracy rates of the PSOLDA were higher than those of these approaches in many public data sets.