Machine Learning
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Medical Data Mining on the Internet: Research on a Cancer Information System
Artificial Intelligence Review - Special issue on data mining on the Internet
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Theoretical Bounds of Majority Voting Performance for a Binary Classification Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Boosting an Associative Classifier
IEEE Transactions on Knowledge and Data Engineering
Approaches to text mining for clinical medical records
Proceedings of the 2006 ACM symposium on Applied computing
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Improved biomedical document retrieval system with PubMed term statistics and expansions
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Orthogonal projection weights in dimension reduction based on Partial Least Squares
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Neural network ensemble with negatively correlated features for cancer classification
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
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An expert system which can support in indexing automatically an ICD-9 code for a clinical diagnosis record is necessary and in great demand for hospitals. The ICD-9 code determines reimbursement amount and an incorrect code may result in fining. In this paper we present a new expert system to index automatically an ICD-9 code with respect to two perspectives. First, the system analyses the free-textual medical documents as would be necessary to activate the uses of natural language process and text mining. A free-textual document has to be represented by a large number of vocabulary words as analogy with a high dimensional data vector. Second, we drive a new ensemble classifier which combines the uses of the majority voting approach with multiple learning algorithms and the boosting approach at the same time. The motivation is stimulated in that when a predicted ICD-9 code of a majority voting of a clinical diagnosis record is incorrect, the record needs to be trained more often. The experimental results show that the proposed ensemble technique is able to achieve simultaneously stability and performance in terms of classification accuracy.