A new approach to gene prediction using the self-organizing map

2004 ◽  
Author(s):  
S. Mahony ◽  
T.J. Smith ◽  
J.O. Mclnerney ◽  
A. Golden
Keyword(s):  
Gene Prediction ◽  
The Self ◽  
Self Organizing Map ◽  
New Approach ◽  
Self Organizing

Approach for Multi-Label Text Data Class Verification and Adjustment Based on Self-Organizing Map and Latent Semantic Analysis

Informatica ◽  
2022 ◽  
pp. 1-22
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
The Self ◽  
Self Organizing Map ◽  
Text Data ◽  
New Approach ◽  
New Class ◽  
Financial News ◽  
Self Organizing

In this paper, a new approach has been proposed for multi-label text data class verification and adjustment. The approach helps to make semi-automated revisions of class assignments to improve the quality of the data. The data quality significantly influences the accuracy of the created models, for example, in classification tasks. It can also be useful for other data analysis tasks. The proposed approach is based on the combination of the usage of the text similarity measure and two methods: latent semantic analysis and self-organizing map. First, the text data must be pre-processed by selecting various filters to clean the data from unnecessary and irrelevant information. Latent semantic analysis has been selected to reduce the vectors dimensionality of the obtained vectors that correspond to each text from the analysed data. The cosine similarity distance has been used to determine which of the multi-label text data class should be changed or adjusted. The self-organizing map has been selected as the key method to detect similarity between text data and make decisions for a new class assignment. The experimental investigation has been performed using the newly collected multi-label text data. Financial news data in the Lithuanian language have been collected from four public websites and classified by experts into ten classes manually. Various parameters of the methods have been analysed, and the influence on the final results has been estimated. The final results are validated by experts. The research proved that the proposed approach could be helpful to verify and adjust multi-label text data classes. 82% of the correct assignments are obtained when the data dimensionality is reduced to 40 using the latent semantic analysis, and the self-organizing map size is reduced from 40 to 5 by step 5.

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.

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.

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
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