By Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero (eds.)
This publication constitutes the refereed lawsuits of the seventh eu convention on Case-Based Reasoning, ECCBR 2004, held in Madrid, Spain in August/September 2004.
The fifty six revised complete papers offered including an invited paper and the summary of an invited speak have been rigorously reviewed and chosen from eighty five submissions. All present concerns in case-based reasoning, starting from theoretical and methodological concerns to complex functions in numerous fields are addressed.
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Additional info for Advances in Case-Based Reasoning: 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 - September 2, 2004. Proceedings
A CBR ontology elaborates and organizes the terminology found in, ideally, any CBR system to provide a domain independent basis for new CBR systems. On this way, CBROnto  elaborates an extensive ontology over CBR terminology, the idea beyond this ontology is to have a common language to deﬁne the elements that compose a CBR system and to be able to build generic CBR methods independent of the knowledge domain. In JColibri the CBR ontology is not represented as a separate resource in a knowledge representation formalism such as OWL or LOOM.
To improve the accuracy of each expert, logistic regression is applied to select a subset of features that can better predict class labels. We also demonstrated that our proposed method improves the prediction accuracy of the TA3 case-based reasoning system on two public lung and leukemia microarray data sets. Although we have used a speciﬁc implementation of a CBR system, our results are applicable in general. Generality of our solution is also not degraded by the application domains, since many other life sciences problem domains are characterized by (ultra) high-dimensionality and a low number of samples.
1. Mixture of Experts: terminal nodes are experts, and the non-terminal node is the gating network. The gating network returns the probability that the input case xi belongs to class Y . As Fig. 2 depicts, the MOE4CBR maintenance method has two main steps: ﬁrst, the case-base of each expert is formed by clustering the data set into k groups, then each case-base is maintained “locally” using feature selection techniques. Each of the k obtained sets will be considered as a case-base for our k CBR experts.
Advances in Case-Based Reasoning: 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 - September 2, 2004. Proceedings by Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero (eds.)