Incremental updating of classification rules

1998 ◽  
pp. 861-870
Author(s):  
J. Shao
Keyword(s):  
Classification Rules ◽  
Incremental Updating

Learning classification rules from an ion chromatography database using a genetic based classifier system

1997 ◽  
Vol 344 (1-2) ◽  
pp. 1-15 ◽  
Author(s):  
A.H.C. van Kampen ◽  
Z. Ramadan ◽  
M. Mulholland ◽  
D.B. Hibbert ◽  
L.M.C. Buydens
Keyword(s):  
Ion Chromatography ◽  
Classification Rules ◽  
Classifier System

KDIGO 2012 Clinical Practice Guideline CKD classification rules out creatinine clearance 24 hour urine collection?

2016 ◽  
Vol 49 (1-2) ◽  
pp. 85-89 ◽  
Author(s):  
A. Ognibene ◽  
G. Grandi ◽  
M. Lorubbio ◽  
S. Rapi ◽  
B. Salvadori ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Creatinine Clearance ◽  
Clinical Practice Guideline ◽  
Practice Guideline ◽  
Urine Collection ◽  
Classification Rules

Incremental Approach for Updating Approximations of Variable Precision Rough Set Based on Dominance Relations

2014 ◽  
Vol 631-632 ◽  
pp. 49-52
Author(s):  
Yan Li ◽  
Jia Jia Hou ◽  
Xiao Qing Liu
Keyword(s):  
Information System ◽  
Rough Set ◽  
Dominance Relation ◽  
Incremental Updating ◽  
Dominance Relations ◽  
Incremental Approach ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Over Time

Variable precision rough set (VPRS) based on dominance relation is an extension of traditional rough set by which can handle preference-ordered information flexibly. This paper focuses on the maintenance of approximations in dominance based VPRS when the objects in an information system vary over time. The incremental updating principles are given as inserting or deleting an object, and some experimental evaluations validates the effectiveness of the proposed method.

Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Jingjing Wang ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Prediction Error ◽  
High Efficiency ◽  
Optimization Problems ◽  
Online Social Network ◽  
Reconstruction Error ◽  
Incremental Updating ◽  
Cp Decomposition ◽  
Error Accumulation

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

Exchange files of geographic incremental updating information based on XML

2009 ◽  
Author(s):  
Ying Shen ◽  
Li Lin ◽  
Wang Hong ◽  
Liu Wanzeng ◽  
Gao Yurong
Keyword(s):  
Incremental Updating
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