Telos: representing knowledge about information systems
ACM Transactions on Information Systems (TOIS)
Machine Learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Self-Organizing Maps
Machine Learning
Incremental Iterative Retrieval and Browsingfor Efficient Conversational CBR Systems
Applied Intelligence
Error-Correcting Output Codes for Local Learners
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Case Representation Issues for Case-Based Reasoning from Ensemble Research
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Intelligent decision support for protein crystal growth
IBM Systems Journal - Deep computing for the life sciences
Data Mining for Case-Based Reasoning in High-Dimensional Biological Domains
IEEE Transactions on Knowledge and Data Engineering
Adaptive mixtures of local experts
Neural Computation
Artificial Intelligence in Medicine
Conceptual Modeling: Foundations and Applications
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It has been shown that an ensemble of classifiers increases the accuracy compared to the member classifiers provided they are diverse. One way to produce this diversity is to base the classifiers on different case-bases. In this paper, we propose the mixture of experts for case-based reasoning (MOE4CBR), where clustering techniques are applied to cluster the case-base into k groups, and each cluster is used as a case-base for our k CBR classifiers. To further improve the prediction accuracy, each CBR classifier applies feature selection techniques to select a subset of features. Therefore, depending on the cases of each case-base, we would have different subsets of features for member classifiers. Our proposed method is applicable to any CBR system; however, in this paper, we demonstrate the improvement achieved by applying the method to a computational framework of a CBR system called TA3. We evaluated the system on two publicly available data sets on mass-to-charge intensities for two ovarian data sets with different number of clusters. The highest classification accuracy is achieved with three and two clusters for the ovarian data set 8-7-02 and data set 4-3-02, respectively. The proposed ensemble method improves the classification accuracy of TA3 from 90% to 99.2% on the ovarian data set 8-7-02, and from 79.2% to 95.4% on the ovarian data set 4-3-02. We also evaluate how individual components in MOE4CBR contribute to accuracy improvement, and we show that feature selection is the most important component followed by the ensemble of classifiers and clustering.