Wolpert and Macready's No Free Lunch theorem proves that no search algorithm is better than any other over all possible discrete functions. The meaning of the No Free Lunch theorem has, however, been the subject of intense debate. We prove that for local neighborhood search on problems of bounded complexity, where complexity is measured in terms of number of basins of attraction in the search s...

primary information about different methods of vegetation sampling is important to researchers to decide about their sampling. in this study we applied five distance methods (closest individual, nearest neighbor, random pairs, point-centered quarter and angle orderd) to estimate plant density in three vegetation types including artemisia sieberi, juncus littoralis and punica granatum in mazanda...

The attribute reduction algorithms based on neighborhood approximation usually use the distance as the approximate metric. Algorithms could result in the loss of information with the same distance threshold to construct the neighborhood families of different dimension spaces. Thereby, an attribute reduction algorithm based on hierarchical granulation is proposed. This algorithm can reduce redun...

Some researchers have proposed using non-Euclidean metrics for clustering data points. Generally, the metric should recognize that two points in the same cluster are close, even if their Euclidean distance is far. Multiple proposals have been suggested, including the Edge-Squared Metric (a specific example of a graph geodesic) and the Nearest Neighbor Metric. In this paper, we prove that the ed...

The use of distance functions in order to determine nearest instance class at Memory Based Learning methods may be crucial when there are no exact matchings. We add relative information over unknown feature values to improve the information extract on the training instances. An experiment was carried out for Spanish Part-Of-Speech tagging of unknown words nding a better performance with our mod...

The instance-based k-nearest neighbor algorithm (KNN)[1] is an effective classification model. Its classification is simply based on a vote within the neighborhood, consisting of k nearest neighbors of the test instance. Recently, researchers have been interested in deploying a more sophisticated local model, such as naive Bayes, within the neighborhood. It is expected that there are no strong ...

This paper presents a path-based distance where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-up-tables or computed on-the-fly. These distances and distance ...

Wolpert and Macready’s No Free Lunch theorem proves that no search algorithm is better than any other over all possible discrete functions. The meaning of the No Free Lunch theorem has, however, been the subject of intense debate. We prove that for local neighborhood search on problems of bounded complexity, where complexity is measured in terms of number of basins of attraction in the search s...

In this article, we introduce a differential evolution based classifier with extension for selecting automatically the applied distance measure from a predefined pool of alternative distances measures to suit optimally for classifying the particular data set at hand. The proposed method extends the earlier differential evolution based nearest prototype classifier by extending the optimization p...