Semi Supervised Relevance Learning for Feature Selection on High Dimensional Data
| Autor: | Alexandros Kalousis, Afef Ben Brahim |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Clustering high-dimensional data
business.industry Computer science Process (engineering) Feature extraction Stability (learning theory) Feature selection 02 engineering and technology Machine learning computer.software_genre Relevance learning Support vector machine 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Curse of dimensionality |
| Zdroj: | AICCSA |
| DOI: | 10.1109/aiccsa.2017.192 |
| Popis: | Nowadays, the advanced technologies make amounts of data growing in a fast paced way. In many application fields, this trend concerns specially dimensions of the data. It is the case where features are about thousands and tens of thousands, while the number of instances is much smaller. This phenomenon is known as the curse of dimensionality and it results in modest classification performance and feature selection instability. In order to deal with this issue, we propose a new feature selection approach that makes use of background knowledge about some dimensions known to be more relevant, as a means of directing the feature selection process. In this approach, prior knowledge about some features is used to learn new relevant features by a semi supervised approach. Experiments on three high dimensional data sets show promising results on both classification performance and stability of feature selection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|