Advances in Case-Based Reasoning: 7th European Conference, by Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero

By Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero (eds.)

This ebook constitutes the refereed court cases 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 provided including an invited paper and the summary of an invited speak have been conscientiously reviewed and chosen from eighty five submissions. All present concerns in case-based reasoning, starting from theoretical and methodological concerns to complicated purposes in a number of fields are addressed.

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Extra resources for Advances in Case-Based Reasoning: 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 - September 2, 2004. Proceedings

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1 Clustering Of the many clustering approaches that have been proposed, only some algorithms are suitable for domains with large number of features and a small number of samples. The two clustering approaches widely used in microarray data analysis [26, 27] are k-means clustering [14] and self-organizing maps (SOMs) [28]. Our earlier evaluation suggests that spectral clustering [17] outperforms k-means clustering and SOMs [25]. The comparison was based on two criteria: 1. Dunn’s index [29], which does not require class labels and identifies how “compact and well separated” clusters are.

Similarity functions can be further parameterized through the context of a particular system configuration. JColibri: An Object-Oriented Framework for Building CBR Systems 41 Reuse. It is well known how hard is to provide generic methods for CBR adaptation. JColibri provides an abstract design where slot-based and case-based adaptation methods can be hooked, along with a number of simple methods for adjusting primitive type values that populate this abstract design. The framework also provides the infrastructure to connect a CBR system with Jess1 , the Java version of CLIPS2 .

Feature selection techniques are classified into filter and wrapper methods [31]. The main difference is that the latter use the final classifier to evaluate the subset of features, while the former do not. Maintaining Case-Based Reasoning Systems: A Machine Learning Approach 23 Fisher criterion and standard t-test are two statistical methods that have been successfully applied to feature selection problem in (ultra) high-dimensional data sets [32]. In order to select a suitable feature selection approach for CBM, we have evaluated performance of Fisher criterion, t-test, and the logistic regression model [18] when used in a CBR classifier [25].

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