Text classification using KM-ELM classifier
| Autor: | K S Neethu, T S Jyothis, Jithin Dev |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning Data classification Pattern recognition Linear classifier 02 engineering and technology Machine learning computer.software_genre Statistical classification 020204 information systems 0202 electrical engineering electronic engineering information engineering One-class classification 020201 artificial intelligence & image processing Artificial intelligence business Feature learning computer Extreme learning machine |
| Zdroj: | 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). |
| DOI: | 10.1109/iccpct.2016.7530338 |
| Popis: | Classification systems adapts many machine learning techniques for quality performance in data classification. The neural networks has some unique characteristics and features which can handle high dimensional features and documents with noise and contradictory data. Classification is important to classify the input text into different domains appropriately. This paper give out a move towards classification of text that combines two machine learning techniques, K-Means and extreme learning machines. First the clustering and feature selection will perform using K-Means algorithm and then this attribute will be the training set for the extreme learning machine. Extreme learning machines nothing but a feed forward neural network without any tuning and has a single hidden layer. The experimental results on different datasets have shown that the combination of machine learning techniques shows a performance improvement. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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