.png){kind=icon}
Return
Center-based nearest neighbor classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2006.06.033.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a novel center-based nearest neighbor (CNN) classifier is proposed to deal with the pattern classification problems. Unlike nearest feature line (NFL) method, CNN considers the line passing through a sample point with known label and the center of the sample class. This line is called the center-based line (CL). These lines seem to have more capacity of representation for sample classes than the original samples and thus can capture more information. Similar to NFL, CNN is based on the nearest distance from an unknown sample point to a certain CL for classification. As a result, the computation time of CNN can be shortened dramatically with less accuracy decrease when compared with NFL. The performance of CNN is demonstrated in one simulation experiment from computational biology and high classification accuracy has been achieved in the leave-one-out test. The comparisons with nearest neighbor (NN) classifier and NFL classifier indicate that this novel classifier achieves competitive performance. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
pattern classification
nearest neighbor
nearest feature line
centered-based nearest neighbor
computational biology
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
No organization information available
Cited Papers
Electrolytes for Li- and Na-Ion Batteries: Concepts, Candidates, and the Role of Nanotechnology
PREAI
ShareSaveUsing neural networks for prediction of the subcellular location of proteins.png)
NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSave
ShareSave no more
no more