Case-Based Reasoning systems retrieve cases using a similarity function based on the K-NN or some derivatives. These functions are sensitive to irrelevant, interacting or noisy features. Many similarity functions weigh the relevance of features to avoid this problem. This article proposes two weighting methods based on Rough Sets theory: Proportional Rough Sets and Dependence Rough Sets. Both w...

In this paper, we investigate the use of a hybrid genetic feature weighting and selection (GEFeWS) algorithm for multi-biometric recognition. Our results show that GEFeWS is able to achieve higher recognition accuracies than using genetic-based feature selection (GEFeS) alone, while using significantly fewer features to achieve approximately the same accuracies as using genetic-based feature we...

Feature Weighting is one of the most difficult tasks when developing Case Based Reasoning applications. This complexity grows when dealing with ill-defined wide domains with a sparse case base. Moreover, most widely-used feature selection and feature weighting methods assume that features are either relevant in the whole instance space or irrelevant through-out. However, it is often the case th...

We investigate the usefulness of evolutionary al gorithms in three incarnations of the problem of feature relevance assignment in memory based language processing MBLP feature weight ing feature ordering and feature selection We use a simple genetic algorithm ga for this problem on two typical tasks in natural lan guage processing morphological synthesis and unknown word tagging We nd that ga f...

This paper presents a new feature weighting method for distance-based classifiers. It is based on a generalized least squares minimization of a criterion function to estimate a feature relevance metric. Experiments over both artificial and real data sets illustrate the behaviour of this algorithm when irrelevant attributes and/or features with varying relevance are present. Effectiveness of the...

One approach to feature detection is to match a parametric model of the feature to the image data. Naturally, the performance of such detectors is highly dependent upon the function used to measure the degree of fit between the feature model and the image data. In this paper, we first show how an existing detector can be extended to use a weighted L norm as the matching function with minimal ex...

A number of network features is used to describe normal and intrusive traffic patterns. However the choice of features is dependent on which pattern to be detected. In order to identify which network features are more important for a particular network pattern, we propose an automated feature weighting method based on a fuzzy subspace approach to vector quantization modeling that can assign a w...

Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the case-base, and feature weighting methods that improve the similarity-based selection of relevant cases. Two main types of method are presented: filter methods ...