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Condensed Nearest Neighbor Data Domain Description

Fabrizio Angiulli
2007 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Then, the Condensed Nearest Neighbor Domain Description (CNNDD) algorithm is described, which computes a reference-consistent subset with only two reference set passes.  ...  A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors' distances in a reference data set, assumed  ...  It is shown that finding the minimum-cardinality reference-consistent subset is a computationally demanding task and the Condensed Nearest Neighbor Domain Description (CNNDD) algorithm is provided, which  ... 
doi:10.1109/tpami.2007.1086 pmid:17699920 fatcat:b7yikkbxnvb6hdw3hxx5p5yqwa

Condensed Nearest Neighbor Data Domain Description [chapter]

Fabrizio Angiulli
2005 Lecture Notes in Computer Science  
Then, the Condensed Nearest Neighbor Domain Description (CNNDD) algorithm is described, which computes a reference-consistent subset with only two reference set passes.  ...  A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors' distances in a reference data set, assumed  ...  It is shown that finding the minimum-cardinality reference-consistent subset is a computationally demanding task and the Condensed Nearest Neighbor Domain Description (CNNDD) algorithm is provided, which  ... 
doi:10.1007/11552253_2 fatcat:cewq6nno6ffgtkcqvtk3l6wfci

DIAGNOSE EFFECTIVE EVOLUTIONARY PROTOTYPE SELECTION USING AN OVERLAPPING MEASURE

SALVADOR GARCÍA, JOSÉ-RAMÓN CANO, ESTER BERNADÓ-MANSILLA, FRANCISCO HERRERA
2009 International journal of pattern recognition and artificial intelligence  
In addition, we have analyzed different k values for the nearest neighbour classifier in this domain of study to see its influence on the results of PS methods.  ...  Evolutionary prototype selection has shown its effectiveness in the past in the prototype selection domain.  ...  Prototype selection algorithms The classical PS algorithms used in this study are: an edition algorithm (Edited Nearest Neighbor 34 ) and a boundary conservative or condensation algorithm (Condensed Nearest  ... 
doi:10.1142/s0218001409007727 fatcat:gxoirsmoerbu5hb5iewe4tgt4i

Dynamic time warping constraint learning for large margin nearest neighbor classification

Daren Yu, Xiao Yu, Qinghua Hu, Jinfu Liu, Anqi Wu
2011 Information Sciences  
Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification.  ...  For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin.  ...  Condensing results We have to compute (n 2 À n)/2 times DTW distance through a LOO nearest neighbor iteration. A condensing technique can be used to reduce the size of samples.  ... 
doi:10.1016/j.ins.2011.03.001 fatcat:4vr43rah3rd7pnduxnfpipeppm

A meta-learning framework for pattern classification by means of data complexity measures

J. M. Sotoca, R. A. Mollineda, J. S. Sanchez
2006 Inteligencia Artificial  
It is widely accepted that the empirical behavior of classifiers strongly depends on available data.  ...  Recently, some researchers have tried to characterize data complexity and relate it to classifier performance.  ...  Let N = 1 (x i ) and N = 1 (x i ) be the intra-class nearest neighbor and the inter-class nearest neighbor of a given example (x i , ω i ), respectively.  ... 
doi:10.4114/ia.v10i29.875 fatcat:3m5swcr6yfd7rdju2nf6vytrz4

Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs [chapter]

Milton García-Borroto, Yenny Villuendas-Rey, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad
2009 Lecture Notes in Computer Science  
Finding a minimal subset of objects that correctly classify the training set for the nearest neighbors classifier has been an active research area in Pattern Recognition and Machine Learning communities  ...  Nearest Neighbor (CNN) algorithm [4] .  ...  Minimal Consistent Subset Selection Finding consistent subsets of objects for the Nearest Neighbor classifier has been a problem of interest in Pattern Recognition since 1968 when Hart proposed the Condensed  ... 
doi:10.1007/978-3-642-10268-4_54 fatcat:mtjuuks2vza7pjy4e626aaw4wu

Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification

Francois Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn Keogh
2014 2014 IEEE International Conference on Data Mining  
that are at least as accurate as their more lethargic Nearest Neighbor cousins.  ...  A commonly used technique to glean the benefits of the Nearest Neighbor algorithm, without inheriting its undesirable time complexity, is to use the Nearest Centroid algorithm.  ...  the contract time.  Drop{X}: There has been significant work on data editing (numerosity reduction/condensing) for nearest neighbor classification [29] .  ... 
doi:10.1109/icdm.2014.27 dblp:conf/icdm/PetitjeanFWNCK14 fatcat:arqqesnfp5fnxk4edregonm2a4

DECISION BOUNDARY PRESERVING PROTOTYPE SELECTION FOR NEAREST NEIGHBOR CLASSIFICATION

RICARDO BARANDELA, FRANCESC J. FERRI, J. SALVADOR SÁNCHEZ
2005 International journal of pattern recognition and artificial intelligence  
The excessive computational resources required by the Nearest Neighbor rule are a major concern for a number of specialists and practitioners in the Pattern Recognition community.  ...  Practical importance of this subject is obvious in many domains, such as data mining, text categorization, remote sensing, and retrieval of multimedia databases, to name a few.  ...  This concept has been extensively used in the literature with different names as, for example, Nearest Unlike Neighbor (NUN) 8 or Nearest Neighbor from the Opposite class (NNO). 25 Ritter et al. introduced  ... 
doi:10.1142/s0218001405004332 fatcat:g4wtwxmvwvdrvkenz6b47nu6sa

Improving the family orientation process in Cuban Special Schools trough Nearest Prototype classification

Y. Villuendas-Rey, C. Rey-Benguria, Y. Caballero-Mota, M. M. Garcia-Lorenzo
2013 International Journal of Interactive Multimedia and Artificial Intelligence  
The proposal obtains very good results on the SABM data, and over repository databases.  ...  The family orientation process in SABM involves clustering and classification of mixed type data with non-symmetric similarity functions.  ...  Condensing methods were proposed first by Hart in 1968 with the Condensed Nearest Neighbor (CNN) algorithm [21] .  ... 
doi:10.9781/ijimai.2013.212 fatcat:2ali66vq5bg7ved5kekjfqaw2u

Experimental Determination of Correlations Between Spontaneously Formed Vortices in a Superconductor

Daniel Golubchik, Emil Polturak, Gad Koren, Boris Y. Shapiro, Irina Shapiro
2011 Journal of Low Temperature Physics  
In the limit where the domains are mono-dispersed and close packed ( fig.3b) , the angle between nearest neighbors will be 120 o .  ...  For each vortex in the array, we look for its two nearest neighbors. Nearest neighbors are defined as vortices located within a distance of ∼ξ and have an opposite polarity (see fig.3 ).  ... 
doi:10.1007/s10909-011-0364-y fatcat:db4exd57dbeqfk24tnyhv64pdq

Comparison of Instances Seletion Algorithms I. Algorithms Survey [chapter]

Norbert Jankowski, Marek Grochowski
2004 Lecture Notes in Computer Science  
Condensation algorithms Condensed Nearest Neighbor Rule (CNN) was made by Hart [4] . The CNN algorithm starts new data set from one instance per class randomly chosen from training set.  ...  Probably the first instance selection algorithm was proposed by Hart in the Condensed Nearest Neighbor Rule (CNN) [4] .  ... 
doi:10.1007/978-3-540-24844-6_90 fatcat:hxcibt4vang75ebg7m7evjikpy

Phase transitions in clusters

R. S. Berry, B. M. Smirnov
2009 Low temperature physics (Woodbury, N.Y., Print)  
Therefore, in considering condensed inert gases, we restrict by interaction between nearest neighbors.  ...  The melting curve for condensed argon: solid signs are experimental data, open circles are constructed on the basis of the results of computer simulation [47] , q is a number of nearest neighbors for  ... 
doi:10.1063/1.3114589 fatcat:hvyfu2zrpnbjjgu6tmq33bnsqu

Disorder-induced phase segregation inLa2/3Ca1/3MnO3manganites

M. García-Hernández, A. Mellergård, F. J. Mompeán, D. Sánchez, A. de Andrés, R. L. McGreevy, J. L. Martínez
2003 Physical Review B (Condensed Matter)  
The results evidence phase separation within a paramagnetic matrix into ferro and antiferromagnetic domains correlated to anistropic lattice distortions in the vicinity of the metal-insulator transition  ...  Therefore, even our classical description of spins existing between nearest neighbors and next-nearest neighbors ͑that is, between pairs of Mn atoms separated by distances 3рrр4 Å and 5рrр6 Å, respectively͒  ...  Figure 4 shows the normalized positive and negative lobes of the nearest-neighbor spin correlations.  ... 
doi:10.1103/physrevb.68.094411 fatcat:gqiafrk7enfvvnd5wiwlw3mh54

Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm

François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn Keogh
2015 Knowledge and Information Systems  
In virtually all domains, the most accurate classifier is the Nearest Neighbor algorithm with Dynamic Time Warping as the distance measure.  ...  " classifiers that are at least as accurate as their more computationally challenged Nearest Neighbor relatives.  ...  the contract time.  Drop{X}: There has been significant work on data editing (numerosity reduction/condensing) for nearest neighbor classification [35] .  ... 
doi:10.1007/s10115-015-0878-8 fatcat:kjuh7dcxhfc7hpajhvqfzaqmue

The condensation transition in zero-range processes with diffusion

E Levine, D Mukamel, G Ziv
2004 Journal of Statistical Mechanics: Theory and Experiment  
We consider a class of one-dimensional urn models whereby particles hop from an urn to its nearest neighbor by a rate which decays with the occupation number k of the departure site as (1+b/k).  ...  Condensation transition which may take place in this model is studied and the (b,alpha) phase diagram is calculated within the mean field approximation and by numerical simulations.  ...  In this model particles hop between nearest neighbor lattice sites with rates ω k which depend only on the number of particles k at the departure site.  ... 
doi:10.1088/1742-5468/2004/05/p05001 fatcat:2tstw7zufzbu5fnxbr7pybhe3y
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