Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
C4.5: programs for machine learning
C4.5: programs for machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
Ranking a random feature for variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature Weighting Methods for Abstract Features Applicable to Motion based Video Indexing
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Concurrency and Computation: Practice & Experience
A novel prostate cancer classification technique using intermediate memory tabu search
EURASIP Journal on Applied Signal Processing
A Revised EM-like Mechanism for Solving the Vehicle Routing Problems
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
A systematic study on attribute reduction with rough sets based on general binary relations
Information Sciences: an International Journal
Electromagnetism-Like Mechanism Based Algorithm for Neural Network Training
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Modified movement force vector in an electromagnetism-like mechanism for global optimization
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
Classification Algorithm Based on Feature Selection and Samples Selection
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Design of nearest neighbor classifiers: multi-objective approach
International Journal of Approximate Reasoning
An improved approach to steganalysis of JPEG images
Information Sciences: an International Journal
Neural Computing and Applications
Information Sciences: an International Journal
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Strengthening learning algorithms by feature discovery
Information Sciences: an International Journal
Unsupervised neural techniques applied to MR brain image segmentation
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
An electromagnetism metaheuristic for solving the Maximum Betweenness Problem
Applied Soft Computing
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
Two metaheuristic approaches for solving multidimensional two-way number partitioning problem
Computers and Operations Research
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In recent years, methods of feature selection have been increasingly emphasized as venues for reducing cost and shortening the length of time required for computation in data mining. This study utilizes electromagnetism-like mechanism as a wrapper approach to feature selection. Birbil and Fang proposed EM in 2003. EM uses the attraction-repulsion mechanism of the electromagnetism theory to ascertain the optimal solution. Although EM has been applied to the topic of optimization in continuous space and a small number of studies on discrete problems, it has not been applied to the subject of feature selection. In this study, EM combined with 1-nearest-neighbor (1NN) was applied for feature selection and classification. This study utilized the total force exerted on a particle and evaluated this force to determine which features are to be selected. The most crucial features were selected according to the proposed method based on the minimum miss-classification rate, which was attained through 1NN. An unknown datum was classified by 1NN based on the chosen reduced model. To estimate the effectiveness of the proposed method, a numerical experiment was conducted using several data sets with diverse sizes, features, separability, and classes. Experimental results indicated that the proposed method outperformed other well-known algorithms in not only balanced classification accuracy but also efficiency of feature selection. Lastly, this study used an actual case concerning gestational diabetes mellitus to demonstrate the workability of the proposed method.