Improving nearest neighbor rule with a simple adaptive distance measure

Wang, Jigang; Neskovic, Predrag; Cooper, Leon N.

doi:10.1016/j.patrec.2006.07.002

Cited by 217 publications

(78 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This closeness is defined by means of the distance d which turns the election of this metric essential, since different metrics will most likely generate different classifications. As a consequence the election of the metric is widely discussed in the literature, as shown in [83]. Note that the other main drawback that this technique presents is the selection of the number of neighbors to consider [84].…”

Section: Related Workmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

View full text Add to dashboard Cite

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

show abstract

Section: Related Workmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

View full text Add to dashboard Cite

show abstract

“…• The Non IS family includes three classifiers that retain all the training instances as knowledge base: KNN, CenterNN [28] and KNNAdaptive [29].…”

Section: Methodsmentioning

confidence: 99%

A Genetic Tuned Fuzzy Classifier Based on Prototypes

Leyva¹,

González²,

Pérez³

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

View full text Add to dashboard Cite

It is known that main drawbacks of the k-Nearest Neighbors classifier are related to the need for keeping all the training prototypes. Although there are several approaches capable to significantly reduce the size of the case base, they damage the classification accuracy. We propose a novel fuzzy approach that significantly reduces the prototypes base and also improves the classification accuracy. Its good performance is evidenced by an experimental study involving 20 prototype based classifiers and 30 databases, in which the proposal is the only approach placed among the top performers in both reduction and accuracy.

show abstract

“…The class label of the unknown sample is then predicted to be the most frequent one occurring in the k nearest-neighbours. The 1NN classifier is well explored in the literature and has been proved to have good classification performance on a wide range of real-world data sets [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%