Machine Learning
Hybrid Intelligent Systems
Machine Learning
Machine Learning
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Multi-layer Perceptrons for Functional Data Analysis: A Projection Based Approach
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Discovering attribute relationships, dependencies and rules by using rough sets
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Soft computing system for bank performance prediction
Applied Soft Computing
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Action rule discovery from incomplete data
Knowledge and Information Systems
Computers in Biology and Medicine
Rough Sets for Handling Imbalanced Data: Combining Filtering and Rule-based Classifiers
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
Tolerance Approximation Spaces
Fundamenta Informaticae
Computers in Biology and Medicine
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A critical option of total hip arthroplasty (THA) is considered only when tried more conservative treatments but continued to have pain, stiffness, or problems with the function of ones hip. THA plays one of major concerns under the waves of the rapid growth of aging populations and the constrained health care resources in Taiwan. Moreover, prior studies indicated that imbalanced class distribution problems do exist in the constructed classification model and cause seriously negative effects on model performances in the health care industry. Therefore, this study proposes an integrated hybrid approach to provide an alternate method for classifying the quality (e.g., the staying length in hospital) of medical practice with an imbalanced class problem after performing a THA procedure for hip replacement patients and their doctors in the health care industry. The proposed approach is constituted by seven components: expert knowledge, global discretization, imbalanced bootstrap technique, reduct and core methods, rough sets, rule induction, and rule filter. The proposed approach is illustrated in practice by examining an experimental dataset from the National Health Insurance Research Database (NHIRD) in Taiwan. The experimental results reveal that the proposed approach has better performance than the listed methods under evaluation criteria. The output created by the rough set LEM2 algorithm is a comprehensible decision rule set that can be applied in knowledge-based health care services as desired. The analytical results provide useful THA information for both academics and practitioners and these results could be applicable to other diseases or to other countries with similar social and cultural practices.