Hierarchical Classification with Jumping Emerging Patterns
| Autor: | Mauri Ferrandin, Luiz Melo Romão |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
General Computer Science
Computer science business.industry 02 engineering and technology Function (mathematics) computer.software_genre medicine.disease_cause Machine learning Task (project management) Multiclass classification Statistical classification ComputingMethodologies_PATTERNRECOGNITION Jumping 020204 information systems Taxonomy (general) 0202 electrical engineering electronic engineering information engineering medicine One-class classification 020201 artificial intelligence & image processing Data mining Artificial intelligence Electrical and Electronic Engineering business computer |
| Zdroj: | IEEE Latin America Transactions. 14:4143-4149 |
| ISSN: | 1548-0992 |
| DOI: | 10.1109/tla.2016.7785945 |
| Popis: | Classification is a common task in Machine Learning and Data Mining. Some classification problems are called hierarchical classification problems because they need to take into account a hierarchical taxonomy which establishes an order between involved classes. The protein's function prediction is considered a hierarchical classification problem because their functions are arranged in a hierarchical taxonomy of classes. This paper presents an algorithm for hierarchical classification using the jumping emerging patterns approach. Jumping emerging patterns have been used to flat classification and in this work we explore its adoption in a hierarchical classification scenario. The proposed algorithm was evaluated in eight real datasets, compared against two other recent hierarchical classification algorithms from the literature and also with flat k-nearest neighbor classification algorithm. Preliminary results showed that the proposed approach is an alternative for hierarchical classification, having as main advantages the simplicity and understandability with good accuracy. |
| Databáze: | OpenAIRE |
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