An unsupervised-based dynamic feature selection for classification tasks
| Autor: | João C. Xavier-Júnior, Carine A. Dantas, Anne M. P. Canuto, Romulo de O. Nunes |
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| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science Correlation clustering Feature extraction 0211 other engineering and technologies Conceptual clustering Feature selection 02 engineering and technology computer.software_genre Machine learning k-nearest neighbors algorithm Biclustering Feature (computer vision) Problem domain 0202 electrical engineering electronic engineering information engineering Canopy clustering algorithm 020201 artificial intelligence & image processing Data mining Artificial intelligence Cluster analysis business computer Selection (genetic algorithm) 021101 geological & geomatics engineering |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2016.7727749 |
| Popis: | Recently, the number of features in different problem domains has grown enormously. In order to select the best representation (attributes) for these problems, a deep knowledge of the problem domain is required. As this type of knowledge is not always possible, feature selection needs to be applied as an automatic selection process of the most relevant attributes in a dataset. In this paper, we propose a new dynamic feature selection technique using data clustering algorithms to select features in a dynamic way and the selected features will be used in classification methods. Our technique aims to select the best attributes for a group of instances rather than to the entire dataset, leading to a dynamic way to select attributes. We will also carry out an empirical analysis using well-known existing methods. Our findings indicated gains when comparing the proposed methods to the existing ones, for the majority of cases. |
| Databáze: | OpenAIRE |
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