PMID- 26479827
OWN - NLM
STAT- MEDLINE
DCOM- 20160923
LR  - 20151123
IS  - 1549-960X (Electronic)
IS  - 1549-9596 (Linking)
VI  - 55
IP  - 11
DP  - 2015 Nov 23
TI  - N3 and BNN: Two New Similarity Based Classification Methods in Comparison with 
      Other Classifiers.
PG  - 2365-74
LID - 10.1021/acs.jcim.5b00326 [doi]
AB  - Two novel classification methods, called N3 (N-nearest neighbors) and BNN (binned 
      nearest neighbors), are proposed. Both methods are inspired by the principles of 
      the K-nearest neighbors (KNN) method, being both based on object pairwise 
      similarities. Their performance was evaluated in comparison with nine well-known 
      classification methods. In order to obtain reliable statistics, several 
      comparisons were performed using 32 different literature data sets, which differ 
      for number of objects, variables and classes. Results highlighted that N3 on 
      average behaves as the most efficient classification method with similar 
      performance to support vector machine based on radial basis function kernel 
      (SVM/RBF). The method BNN showed on average higher performance than the classical 
      K-nearest neighbors method.
FAU - Todeschini, Roberto
AU  - Todeschini R
AD  - Milano Chemometrics and QSAR Research Group, Department of Earth and 
      Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 
      20126 Milan, Italy.
FAU - Ballabio, Davide
AU  - Ballabio D
AD  - Milano Chemometrics and QSAR Research Group, Department of Earth and 
      Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 
      20126 Milan, Italy.
FAU - Cassotti, Matteo
AU  - Cassotti M
AD  - Milano Chemometrics and QSAR Research Group, Department of Earth and 
      Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 
      20126 Milan, Italy.
FAU - Consonni, Viviana
AU  - Consonni V
AD  - Milano Chemometrics and QSAR Research Group, Department of Earth and 
      Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 
      20126 Milan, Italy.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20151102
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
SB  - IM
MH  - Algorithms
MH  - Animals
MH  - *Artificial Intelligence
MH  - Databases, Factual
MH  - Humans
MH  - Pattern Recognition, Automated/*methods
MH  - Software
EDAT- 2015/10/20 06:00
MHDA- 2016/09/24 06:00
CRDT- 2015/10/20 06:00
PHST- 2015/10/20 06:00 [entrez]
PHST- 2015/10/20 06:00 [pubmed]
PHST- 2016/09/24 06:00 [medline]
AID - 10.1021/acs.jcim.5b00326 [doi]
PST - ppublish
SO  - J Chem Inf Model. 2015 Nov 23;55(11):2365-74. doi: 10.1021/acs.jcim.5b00326. Epub 
      2015 Nov 2.