BDF: A new decision forest algorithm
Autor: | Michael Bewong, Zahidul Islam, Ryan H.L. Ip, Nasim Adnan |
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Rok vydání: | 2021 |
Předmět: |
Information Systems and Management
Computer science 05 social sciences Rank (computer programming) Decision tree 050301 education 02 engineering and technology Base (topology) Computer Science Applications Theoretical Computer Science Random forest Set (abstract data type) Artificial Intelligence Control and Systems Engineering Synchronization (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0503 education Algorithm Software Diversity (business) |
Zdroj: | Information Sciences. 569:687-705 |
ISSN: | 0020-0255 |
Popis: | The foremost requirement for a decision forest to achieve better ensemble accuracy is building a set of accurate and diverse individual decision trees as base classifiers . Existing decision forest algorithms mainly differ from each other on how they induce diversity among the decision trees. At the same time, most of the drawbacks of existing algorithms originate from their induction processes of diversity. In this paper, we propose a new decision forest algorithm that is more balanced through effective synchronization between different sources of diversity. The proposed algorithm is balanced theoretically and empirically. We carried out experiments on 25 well-known data sets that are publicly available from the UCI Machine Learning Repository, to perform an extensive empirical evaluation. The experimental results indicate that the proposed algorithm has the best average ensemble accuracy rank of 1.8 compared to its closest competitor at 3.5. Using the Friedman and Bonferroni-Dunn tests, we also show that such an improvement is indeed statistically significant. In addition, the proposed algorithm is found to be competitive in terms of complexity and other relevant parameters. |
Databáze: | OpenAIRE |
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