An investigation into the effect of unlabeled neurons on Self-Organizing Maps
| Autor: | Andries P. Engelbrecht, Willem S. van Heerden |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Self-organizing map
TheoryofComputation_COMPUTATIONBYABSTRACTDEVICES Artificial neural network Computer science business.industry 02 engineering and technology Machine learning computer.software_genre ComputingMethodologies_ARTIFICIALINTELLIGENCE Data modeling Exploratory data analysis ComputingMethodologies_PATTERNRECOGNITION medicine.anatomical_structure nervous system 020204 information systems 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Neuron Artificial intelligence Cluster analysis business computer |
| Zdroj: | SSCI |
| DOI: | 10.1109/ssci.2016.7849938 |
| Popis: | Self-Organizing Maps (SOMs) are unsupervised neural networks that build data models. Neuron labeling attaches descriptive textual labels to the neurons making up a SOM, and is an important component of SOM-based exploratory data analysis (EDA) and data mining (DM). Several neuron labeling approaches tend to leave some neurons unlabeled. The interaction between unlabeled neurons and SOM model accuracy affect the choice of labeling algorithm for SOM-based EDA and DM, but has not been previously investigated. This paper applies the widely used example-centric neuron labeling algorithm to several classification problems, and empirically investigates the relationship between the percentage of neurons left unlabeled and classification accuracy. Practical recommendations are also presented, which address the treatment of unlabeled neurons and the selection of an appropriate neuron labeling algorithm. |
| Databáze: | OpenAIRE |
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