An optimization of ReliefF for classification in large datasets
| Autor: | Yue Huang, Norman D. Black, Paul McCullagh |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Information Systems and Management
business.industry Computer science Computation Pattern recognition Feature selection Feature selector Machine learning computer.software_genre Clinical diabetes Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer Selection (genetic algorithm) |
| Zdroj: | Data & Knowledge Engineering. 68:1348-1356 |
| ISSN: | 0169-023X |
| DOI: | 10.1016/j.datak.2009.07.011 |
| Popis: | ReliefF has proved to be a successful feature selector but when handling a large dataset, it is computationally expensive. We present an optimization using Supervised Model Construction which improves starter selection. Effectiveness has been evaluated using 12 UCI datasets and a clinical diabetes database. Experiments indicate that compared with ReliefF, the proposed method improved computation efficiency whilst maintaining the classification accuracy. In the clinical dataset (20,000 records with 47 features), feature selection via Supervised Model Construction (FSSMC) reduced the processing time by 80%, compared to ReliefF, and maintained accuracy for Naive Bayes, IB1 and C4.5 classifiers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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