Fundamentals of algorithmics
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Customer Retention via Data Mining
Artificial Intelligence Review - Issues on the application of data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient content-based image retrieval using automatic feature selection
ISCV '95 Proceedings of the International Symposium on Computer Vision
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
The impact of socialization on the role adjustment of information systems professionals
Journal of Management Information Systems - Special section: Managing virtual workplaces and teleworking with information technology
Business intelligence systems: a comparative analysis
WSEAS Transactions on Information Science and Applications
Classification of personal Arabic handwritten documents
WSEAS Transactions on Information Science and Applications
Face recognition based on multi-scale singular value features
WSEAS Transactions on Computers
Domain driven data mining in human resource management: A review of current research
Expert Systems with Applications: An International Journal
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This study proposed to address a new method that could select subsets more efficiently. In addition, the reasons why employers voluntarily turnover were also investigated in order to increase the classification accuracy and to help managers to prevent employers' turnover. The mixed feature subset selection used in this study combined Taguchi method and Nearest Neighbor Classification Rules to select feature subset and analyze the factors to find the best predictor of employer turnover. All the samples used in this study were from industry A, in which the employers left their job during 1st of February, 2001 to 31st of December, 2007, compared with those incumbents. The results showed that through the mixed feature subset selection method, total 18 factors were found that are important to the employers. In addition, the accuracy of correct selection was 87.85% which was higher than before using this feature subset selection method (80.93%). The new feature subset selection method addressed in this study does not only provide industries to understand the reasons of employers' turnover, but also could be a long-term classification prediction for industries.