Learning feature-projection based classifiers
| Autor: | Aynur A. Dayanik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Classification learning
Computer science Linear classifier Learning algorithms Machine learning computer.software_genre Classification algorithm k-nearest neighbors algorithm Artificial Intelligence Boundary noise One-class classification Space requirements Probabilistic analysis of algorithms UCI repository Feature projections Weighted Majority Algorithm Feature classification Classification (of information) business.industry General Engineering Pattern recognition Feature projection Inductive learning Computer Science Applications Projection (relational algebra) Statistical classification ComputingMethodologies_PATTERNRECOGNITION Feature (computer vision) Artificial intelligence Data sets business computer |
| Zdroj: | Expert Systems with Applications Expert Systems with Applications: an international journal |
| Popis: | This paper aims at designing better performing feature-projection based classification algorithms and presents two new such algorithms. These algorithms are batch supervised learning algorithms and represent induced classification knowledge as feature intervals. In both algorithms, each feature participates in the classification by giving real-valued votes to classes. The prediction for an unseen example is the class receiving the highest vote. The first algorithm, OFP.MC, learns on each feature pairwise disjoint intervals which minimize feature classification error. The second algorithm. GFP.MC, constructs feature intervals by greedily improving the feature classification error. The new algorithms are empirically evaluated on twenty datasets from the UCI repository and are compared with the existing feature-projection based classification algorithms (FILIF, VFI5, CFP, k-NNFP, and NBC). The experiments demonstrate that the OFP.MC algorithm outperforms other feature-projection based classification algorithms. The GFP.MC algorithm is slightly inferior to the OFP.MC algorithm, but, if it is used for datasets with large number of instances, then it reduces the space requirement of the OFP.MC algorithm. The new algorithms are insensitive to boundary noise unlike the other feature-projection based classification algorithms considered here. (C) 2011 Elsevier Ltd. All rights reserved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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