In this paper, we present two novel class-based weighting methods for the Euclidean nearest neighbor algorithm and compare them with global weighting methods considering empirical results on a widely accepted time series classification benchmark dataset. Our methods provide higher accuracy than every global weighting in nearly half of the cases and they have better overall performance. We concl...

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 a network, a distributed consensus algorithm is fully characterized by its weighting matrix. Although there exist numerical methods for obtaining the optimal weighting matrix, we have not found an in-network implementation of any of these methods that works for all network topologies. In this paper, we propose an in-network algorithm for finding such an optimal weighting matrix.

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to ha...

The paper comparatively studies methods of feature weighting in application to the task of cooccurrence-based classification of words according to their meaning. We explore parameter optimization of several weighting methods frequently used for similar problems such as text classification. We find that successful application of all the methods crucially depends on a number of parameters; only a...