Deep Forest: Towards An Alternative to Deep Neural Networks
Novelis (Canada) · Nanjing University
Abstract
In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in our experiments, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient, and users can control training cost according to computational resource available. The efficiency may be further enhanced because gcForest is naturally apt to parallel implementation. Furthermore, in contrast to deep…
Citation impact
- FWCI
- 86.15
- Percentile
- 100%
- References
- 34
Authors
2Topics & keywords
- Computer science
- Artificial intelligence
- Deep neural networks
- Deep learning
- Artificial neural network
- Machine learning
- Process (computing)
- Contrast (vision)