WITHDRAWN: Random forest algorithms for the classification of tree-based ensemble
| Autor: | C. Kishor Kumar Reddy, P. R. Anisha, B. V. Ramana Murthy, R. Madana Mohana |
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| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Computer science Decision tree Brute-force search 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Random forest Tree (data structure) Statistical classification ComputingMethodologies_PATTERNRECOGNITION Search algorithm 0103 physical sciences Classifier (linguistics) 0210 nano-technology Algorithm Statistical hypothesis testing |
| Zdroj: | Materials Today: Proceedings. |
| ISSN: | 2214-7853 |
| Popis: | Random Forest is one of the widely used tree-based ensemble classification algorithms. Many aspects of building tree ensembles are introduced to reduce correlation among decision trees within the forest. Bootstrap is used in Random Forest to reduce bias decision tree and to decide splits in every decision tree. Classification by Ensembles from Random Partitions (CERP) is a different algorithm to create an ensemble. CERP randomly partitions the data instead of using bootstrap and creates multiple ensembles instead of one. A forest consists of several decision trees, an ensemble of trees. While Random Forest builds a forest, CERP builds an ensemble of forests. A base classifier in Random Forest uses an exhaustive search to find a split. On the other hand, the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm uses statistical hypothesis testing, which is faster than exhaustively search algorithms and is able to detect interaction using a statistical method. This work investigated tree-based ensemble classification algorithms that include the CERP, GUIDE, and Random Forest for genetic data. |
| Databáze: | OpenAIRE |
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