VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

2005 ◽  
Vol 15 (01n02) ◽  
pp. 101-110 ◽  
Author(s):  
TIMO SIMILÄ ◽  
SAMPSA LAINE
Keyword(s):  
Variable Selection ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Robust Method ◽  
Relevant Variables ◽  
Visual Approach ◽  
Predefined Criterion ◽  
Target Data ◽  
Self Organizing

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.

scholarly journals Saviorganizuojančių neuroninių tinklų sistemų lyginamoji analizė

2009 ◽  
Vol 50 ◽  
pp. 334-339
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
Data Clustering ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Learning Rules ◽  
Main Target ◽  
Similarities And Differences ◽  
Graphical Presentation ◽  
Self Organizing

Straipsnyje nagrinėjamos ir lyginamos tarpusavyje trys saviorganizuojančių neuroninių tinklų (SOM) sistemos: NeNet, SOM-Toolbox ir Databionic ESOM. Pagrindinis šių sistemų tikslas yra suskirstyti duomenis į klasterius pagal jų panašumą, pateikti juos SOM žemėlapyje. Sistemos viena nuo kitos skiriasi duomenų pateikimu, mokymo taisyklėmis, vizualizavimo galimybėmis, todėl čia aptariami sistemų panašumai ir skirtumai. SOM žemėlapiams mokyti ir vizualizuoti naudojami irisų ir stikloduomenys.Comparative Analysis of Self-Organizing Map SystemsPavel Stefanovič, Olga Kurasova SummaryIn the article, we compare three systems of self-organizing maps: NeNet, SOM-Toolbox and Databionic ESOM. The main target of the usage of the systems is data clustering and their graphical presentation on the self-organizing map (SOM). The self-organizing maps are one of types of artifi cial neural networks. The SOM systems are different one from other in their interfaces, the data pre-processing, learning rules, visualization manners, etc. Similarities and differences of the systems have been highlighted here. The experiments have been carried out with two data sets: iris and glass. Quantization and topographic errors of SOMs have been estimated, too.an>

scholarly journals Segmentation of spectroscopic images of the low solar atmosphere by the Self Organizing Map technique

2021 ◽  
Author(s):  
F Schilliro ◽  
P Romano
Keyword(s):  
Semantic Segmentation ◽  
The Self ◽  
Solar Photosphere ◽  
Data Sets ◽  
Self Organizing Map ◽  
Machine Learning Approach ◽  
Fine Structures ◽  
Focus Plane ◽  
Resolution Dataset ◽  
Self Organizing

Abstract We describe the application of Semantic Segmentation by using the Self Organizing Map technique to an high spatial and spectral resolution dataset acquired along the Hα line at 656.28 nm by the Interferometric Bi-dimensional Spectrometer installed at the focus plane of the Dunn Solar Telescope. This machine learning approach allowed us to identify several features corresponding to the main structures of the solar photosphere and chromosphere. The obtained results show the capability and flexibility of this method to identifying and analyzing the fine structures which characterize the solar activity in the low atmosphere. This is a first successful application of the SOM technique to astrophysical data sets.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

Representation and Identification of Fault Conditions of an Anaesthesia System by Means of the Self-Organizing Map

ICANN ’94 ◽  
1994 ◽  
pp. 350-353 ◽  
Author(s):  
Mauri Vapola ◽  
Olli Simula ◽  
Teuvo Kohonen ◽  
Pekka Meriläinen
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

scholarly journals Robust classification with reject option using the self-organizing map

2015 ◽  
Vol 26 (7) ◽  
pp. 1603-1619 ◽  
Author(s):  
Ricardo Gamelas Sousa ◽  
Ajalmar R. Rocha Neto ◽  
Jaime S. Cardoso ◽  
Guilherme A. Barreto
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Reject Option ◽  
Robust Classification ◽  
Self Organizing
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