scholarly journals Application of self-organizing maps to genetic algorithms

2009 ◽  
Author(s):  
S. Kan ◽  
Z. Fei ◽  
E. Kita
Keyword(s):  
Genetic Algorithms ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Social Network Community Detection by Combining Self-Organizing Maps and Genetic Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mehdi Ellouze
Keyword(s):  
Social Networks ◽  
Genetic Algorithms ◽  
Social Network ◽  
Community Detection ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
The Social ◽  
Two Stages ◽  
Self Organizing

Social networks have become an important source of information from which we can extract valuable indicators that can be used in many fields such as marketing, statistics, and advertising among others. To this end, many research works in the literature offer users some tools that can help them take advantage of this mine of information. Community detection is one of these tools and aims to detect a set of entities that share some features within a social network. We have taken part in this effort, and we proposed an approach mainly based on pattern recognition techniques. The novelty of this approach is that we do not directly tackle the social networks to find these communities. We rather proceeded in two stages; first, we detected community cores through a special type of self-organizing map called the Growing Hierarchical Self-Organizing Map (GHSOM). In the second stage, the agglomerations resulting from GHSOM were grouped to retrieve the final communities. The quality of the final partition would be under the control of an evaluation function that is maximized through genetic algorithms. Our system was tested on real and artificial databases, and the obtained results are really encouraging.

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems

2004 ◽  
Vol 18 (7-9) ◽  
pp. 483-493 ◽  
Author(s):  
Ersin Bayram ◽  
Peter Santago ◽  
Rebecca Harris ◽  
Yun-De Xiao ◽  
Aaron J. Clauset ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Optimization of supervised self-organizing maps with genetic algorithms for classification electrophoretic profiles

2014 ◽  
Vol 33 (1) ◽  
pp. 65
Author(s):  
Natalia Tomovska ◽  
Igor Kuzmanovski ◽  
Kiro Stojanoski
Keyword(s):  
Multiple Sclerosis ◽  
Genetic Algorithms ◽  
Cerebrospinal Fluid ◽  
External Validation ◽  
Disc Electrophoresis ◽  
Classification Model ◽  
Self Organizing Maps ◽  
Data Set ◽  
Oligoclonal Igg ◽  
Self Organizing

<p>Standard electrophoresis methods were used in the classification of analyzed proteins in cerebrospinal fluid from patients with multiple sclerosis. Disc electrophoresis was carried out for detection of oligoclonal IgG bands in cerebrospinal fluid on polyacrylamide gel, mainly with multiple sclerosis and other central nervous system dysfunctions. ImageMaster 1D Elite and GelPro specialized software packages were used for fast accurate image and gel analysis. The classification model was based on supervised self-organizing maps. In order to perform the modeling in automated manner genetic algorithms were used. Using this approach and a data set composed of 69 samples we were able to develop models based on supervised self-organizing maps which were able to correctly classify 83 % of the samples in the data set used for external validation.</p>

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