Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data
The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …
technique. The main drawback of this technique appears when massive amounts of data …
Touch-based continuous mobile device authentication: State-of-the-art, challenges and opportunities
The advancement in the computational capability and storage size of a modern mobile
device has evolved it into a multi-purpose smart device for individual and business needs …
device has evolved it into a multi-purpose smart device for individual and business needs …
Classification in the presence of label noise: a survey
B Frénay, M Verleysen - IEEE transactions on neural networks …, 2013 - ieeexplore.ieee.org
Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …
consequences. For example, the accuracy of predictions may decrease, whereas the …
An efficient instance selection algorithm for k nearest neighbor regression
Y Song, J Liang, J Lu, X Zhao - Neurocomputing, 2017 - Elsevier
Abstract The k-Nearest Neighbor algorithm (kNN) is an algorithm that is very simple to
understand for classification or regression. It is also a lazy algorithm that does not use the …
understand for classification or regression. It is also a lazy algorithm that does not use the …
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …
sets that can be considered correct and useful for further data mining algorithms. This paper …
Prototype selection for nearest neighbor classification: Taxonomy and empirical study
The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …
performing recognition tasks. It has also demonstrated itself to be one of the most useful …
Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection
D Wang, D Miao, C Xie - Expert Systems with Applications, 2011 - Elsevier
In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic
seizure detection is proposed. The system includes the following three stages:(i) original …
seizure detection is proposed. The system includes the following three stages:(i) original …
New applications of ensembles of classifiers
R Barandela, RM Valdovinos, JS Sánchez - Pattern Analysis & …, 2003 - Springer
Combination (ensembles) of classifiers is now a well established research line. It has been
observed that the predictive accuracy of a combination of independent classifiers excels that …
observed that the predictive accuracy of a combination of independent classifiers excels that …
Prototype selection for dissimilarity-based classifiers
E Pękalska, RPW Duin, P Paclík - Pattern Recognition, 2006 - Elsevier
A conventional way to discriminate between objects represented by dissimilarities is the
nearest neighbor method. A more efficient and sometimes a more accurate solution is …
nearest neighbor method. A more efficient and sometimes a more accurate solution is …
[图书][B] Graph classification and clustering based on vector space embedding
This book is concerned with a fundamentally novel approach to graph-based pattern
recognition based on vector space embedding of graphs. It aims at condensing the high …
recognition based on vector space embedding of graphs. It aims at condensing the high …